Center for Biosystems

Center for Information Technologies

Center for Innovation and Business Development

Center for Product Development

Center for sensing technologies

Project aim: ANTARES aims to evolve BioSense Institute into a European Centre of Excellence (CoE) for advanced technologies in sustainable agriculture. We strive to deliver disruptive digital solutions to the European farming sector, boost research excellence, stimulate entrepreneurship and employment at regional level, and secure enough safe food for growing global population.
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About the project: To achieve this, ANTARES will focus on three pillars of sustainability: strong research infrastructure that includes the new building and state-of-the-art equipment, dedicated human resources plan, and scientific excellence. A quantum leap in human resources and research infrastructure enabled by ANTARES will level-up scientific capabilities of BioSense. The Institute will explore scientific and technological frontiers across various disciplines: material science, micro and nano electronics, sensor design, remote sensing, Internet of Things, artificial intelligence, and biosystem research.

Prinicpal Research Fellow in the field of natural-mathematical sciences - physics

Vesna Bengin

Alan is a Senior Researcher at the BioSense Institute. He obtained his PhD in Chemistry from Saint-Petersburg State University (2014).
From 2015 to 2023 he held Team Leader position at the Scientific department in Biotechnology company in Saint-Petersburg, where he worked on creation of key components of diagnostic systems for medicine. He also participated in industrial projects and obtained funding from national grants. Currently he is involved in the ANTARES project. His main focus is on the creation of nano and microparticles and their application in sensing systems. His research interests also include organic synthesis, bioconjugation, and immunoassays.

Alan Fidarov

Sonja holds both a Bachelor's and Master's degree in Psychology from the Faculty of Philosophy at the University of Novi Sad. Her professional journey began in 2015 when she embarked on a career as a Clinical Psychologist within the healthcare sector. In 2019, she transitioned into the realm of human resources management.

Her responsibilities at the Institute BioSense include a wide range of functions related to human resources: talent acquisition, recruitment and selection, formulation and implementation of rules and procedures for better work and development of BioSense, employee development, organization of employee training, cooperation with the communications manager with the aim of promoting BioSense and the Institute's employees.

Sonja Tatarin Ragaji

Kristina Kalkan is a Research Assistant at BioSense Institute. She received her PhD in 2021. at Faculty of Sciences from the University of Novi Sad. The topic of her PhD was the estimation of soil erosion rates using fallout radionuclides. She is interested in soil science (modeling soil erosion rates, soil radioactivity, soil magnetic susceptibility), geomorphology, climate, and radioecology research, radio aerosols and natural hazards.  Also, she uses GIS, Remote Sensing, and Drone Imagery for analytical and visualization purposes.

Kristina Kalkan

Ms. Vladislava Ćulum joined the BioSense team in 2015. She has over 20 years of experience in financing and accounting. She has been involved in the development of strategic plans, budget planning, finance and internal controlling. She dealt with forecasting future financial trends and analyzing market trends and competitors.  After graduating high school, she continued her education at Faculty of Philosophy, University of Novi Sad. She is currently working as a financial – accounting analyst at Financial Department in BioSense Institute.

Vladislava Ćulum

Ms. Gorana Lisjuk (F), is employed as a financial - accounting analyst for international projects at Financial Department in BioSense Institute. She is a University of Novi Sad graduate, obtaining her BA degree at the Faculty of Economics, Department of European Economics and Business. She joined the BioSense team in 2016 as an Innovation and Business Development assistant where she gained experience in proposal writing, project implementation, administration and financial management, preparation of financial reports and preparation and submission of deliverables.

Gorana Lisjuk

Zoran Pavlović was born in 1986. in Aleksinac, Serbia. He obtained a Diploma in 2010., and an M.Sc. degree in 2011. in Chemical Engineering at the Faculty of Technology and Metallurgy, University of Belgrade. Master thesis about electrochemical catalysts and cyclone flow reactor fuel cell system development was done at the Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany. He obtained a Ph.D. degree in 2017. in Heterogeneous Electrochemical catalysis (Dr. Rer. Nat - Doctor of Natural Sciences), from the Max Planck Institute for Chemical Energy Conversion, Muelheim an der Ruhr, Germany, as well as from Fritz Haber Institute. Berlin, Germany. During his Ph.D. studies, he presented his work in various electrochemical conferences in Germany and at the Electrochemical society conferences in the USA (Orlando - Florida, and San Diego - California). Since March 2018. he has been working in Pharmaceutical company Phytonet AG, Zurich, Switzerland, where he is involved in starting up a production site of Pharmaceutical food supplement products in Novi Sad, Serbia. He works there as a Production Manager and Manager of Technical systems while doing Process development, Pharmaceutical Technology Transfer, Quality control, and Business development. At the same time since March 2018. he has been working as an Assistant Professor at the Faculty of Pharmacy, the University of Business Academy in Novi Sad, where he gives lectures on Industrial Pharmacy, Organic, Bioorganic, Stereo, and Steroid Chemistry, and supervise B.Sc., M.Sc., and Ph.D. students during their thesis development, and during their practical training in Phytonet AG. Since January 2021, Zoran has been working at the BioSense Institute as a Senior Researcher, joining on many ongoing European projects. His main research interest is the electrochemical catalysis in renewable energy storage, development of bio-electrochemical sensors and materials for applications in agriculture, medicine, pharmacy, and environmental protection.

Zoran Pavlović

Prof. Vesna Bengin is the co-founder of the BioSense Institute - Research and Development Institute for Information Technologies in Biosystems, which focuses on multidisciplinary, game-changing, and needs-driven research of advanced ICT technologies and its applications in biosystems, primarily agriculture. She is the coordinator of H2020 project ANTARES, ranked as the first in the most prestigious H2020 call Teaming which aims to evolve BioSense Institute into a market-oriented European Centre of research excellence. With a budget of € 30 million, ANTARES is the largest research project ever funded in the Republic of Serbia by the European Union and, besides developing institutional scientific excellence, Prof. Bengin also directly focuses on fostering the policy development and economic growth of the region, through specifically designed activities involving the policy makers and national and regional agrifood and IT sectors.

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Prof. Bengin holds the highest national degrees both in teaching and research: she is a full professor at the Department of Physics, University of Novi Sad, as well as the scientific advisor (principal research fellow) elected by the Ministry of education, science, and technology of the Republic of Serbia. She also holds the title of an Extraordinary Professor at Stellenbosch University in South Africa, as well as the position of a Visiting Scholar at the University of Pennsylvania (UPENN), USA. She has been a member of the Management Board of BioSense Institute since its establishment in 2015 until today, and the Vice-president of the same Board in 2015-2019. She has served as the member of the Management Board of the Innovation Fund of the Republic of Serbia in 2011-2017, and as a member of many European and international professional bodies such as the General Assembly of the European Microwave Association, and the Implementation Board of the Group on Earth Observations (GEO). Following the invitation of the European Commission, Prof. Bengin has served as a mid-term evaluator of the entire € 300 million worth intergovernmental framework programme COST (Co-operation in Science and Technology) financed by the European Union, where she has evaluated the political, scientific, and operational aspects of the programme. She has worked as a consultant for Fonds National de la Recherche of Luxembourg in 2017, as well as for a number of research institutions in Turkey and Slovenia in 2016. Since 2019, Prof. Bengin serves as a member of UNICEF Business Advisory Board in Serbia and a consultant for UN FAO (United Nations – Food and Agriculture Organization).

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Prof. Bengin obtained her PhD in 2006 from the University of Novi Sad in the field of microelectronics, has led or participated in 8 FP7, 5 Horizon 2020 and many other international research projects, authored more than 100 scientific papers and published two books with world-renowned publishers (Cambridge University Press and John Wiley &amp; Sons). Leading multidisciplinary research with a focus on the applications, he is also an active promoter of the importance of science with a large experience in public communications.

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Prof. Bengin is the recipient of a number of national and international awards including the special award granted by the European Commission at ‘Marie Curie Actions for an Innovative Europe: Excellence, mobility and skills for researchers, the award for outstanding women in science and technology She's Mercedes awarded by the Mercedes-Benz company, and many more.
She is a mother of three and a great promoter of active involvement of women in STEM.

Slobodan Birgermajer is research associate in the Center for Sensing Technologies at the BioSense Institute. Mr. Birgermajer received his PhD in microware electronics form the Faculty of Technical Sciences, University of Novi Sad, in 2019. He is currently working on development and application of the sensors in agriculture, food industry, and environmental monitoring, and he is actively involved in many H2020 projects such as ANTARES, IPANEMA SHealty, etc. His main research interest includes microwave passive devices, photonic crystals, microfluidic sensors, and 3D and 4D printing.

Slobodan Birgermajer

Dr Nikolina Janković (F) senior researcher in the Center for Sensing Technologies in the BioSense Institute. Author and co-author of more than 40 journal and conference papers and 5 book chapters. Main research interest metamaterials, plasmonics, acoustics, sensors, and microwave passive devices. The coordinator of the H2020 MSCA-RISE project NOCTURNO and EUREKA project WaQUMoS in Serbia. Well versed in EU regulations and procedures and the EC funded projects. In the last seven years, participated in two FP7 projects: MultiWaveS and InnoSense (Financial and Administrative Manager and WP leader), as well as in three H2020 projects (H2020 Teaming ANTARES, NOCTURNO, and SHealthy).

Nikolina Janković

Goran Kitić works at the position of the Head of the Center for Sensing Technologies within the BioSense Institute. He received a PhD degree from the Faculty of Technical Sciences, University of Novi Sad, Serbia in 2016 on the subject of microwave soil moisture sensors. Goran Kitić developed experience in the field of different fabrication technologies (LTCC, thin&amp;thick film technology, microfabrication, PCB, wire bonding, machining, and 3D printing) and characterization methods (electron microscopy, EDS spectroscopy, UV/Vis spectroscopy, optical profilometry, electrical measurements). His research interests are related to the development of sensors applied in agriculture, food sector and environmental protection. Some of the developed sensor prototypes involve a multispectral sensor for plant status assessment, soil moisture sensor, a device for early mastitis detection, nitrates in water detector. Besides stated, he is heavily involved in the development of a robotic system for soil sampling and analysis. His research work resulted in seven international journal publications, and numerous papers published on international conferences. During his research career, he was involved in 13 international and national projects.

Goran Kitić

In 2009, Georges Dubourg received the engineer degree in electronics from the University of Sciences and Technologies in Bordeaux, France. Afterwards, he received his Ph.D degree in microelectronic from the University of Bordeaux in 2012. During his Ph.D thesis, he introduced a new activity in the IMS laboratory concerning organic MEMS for biosensing applications. From 2013 to 2014, he worked in the flied of microfluidic as a postdoctoral research fellow in the Institute of Electronics, Microelectronics and Nanotechnology (IEMN) at the University of Lille, France. Then he undertook postdoctoral research in Serbia at the University of Novi Sad before taking up his current senior researcher position at BioSense Institute in which he has launched from scratch a new research activity in the field of flexible electronic and sensors. His research interests include the design, fabrication and characterization of flexible sensors and microfluidic devices for biomedical application. From 2018, Georges is project coordinator of CISTEM project (H2020‐MSCA‐RISE‐2017) which aims to develop a new generation of organ-on-chip for modeling cardiovascular diseases.

Georges Dubourg

Đorđe Vujić is a Junior research assistant at the Center for Information Technologies at the BioSens Institute in Novi Sad. He holds both a Bachelor's and a Master's degree in Organic Chemistry from the Faculty of Sciences, University of Novi Sad. His undergraduate thesis focused on the “Synthesis of C-homo lactam derivatives of bile acids,” while his master's research was titled “Investigation of the content of selected monoaromatic and chlorinated hydrocarbons in the Danube using a portable mass spectrometer.”

Following his studies, he worked in a laboratory specializing in food contaminant analysis, where he gained hands-on experience with pesticide residues, mycotoxins, and acrylamide. Additionally, he was involved in food quality analysis, including the determination of sugar, amino acid, preservative, artificial sweetener, and vitamin content.

He is currently pursuing a PhD at the Faculty of Sciences in Novi Sad, continuing his work in the field of chemical analysis and environmental monitoring.

<a href="https://orcid.org/0000-0002-7055-9307">ORCID Profil</a>

Đorđe Vujić

Đorđe Dragojević currently works as junior researcher at the Center for Information Technologies, Biosense Institute, University of Novi Sad. He holds an BSc in Mechanical Engineering from the Faculty of Technical Sciences, University of Novi Sad with bachelor degree thesis „Determining the roll-over limit and assesment of the lateral stability of heavy goods vehicles“. During his studies he was involved in several projects at Biosystems Engineering Department, tasked with agricultural machinery and equipment design. In a professional domain, for over 8 years Đorđe contributed as a mechanical designer in civil engineering and automotive industries on a variety of projects,domestic and international. Driven by curiosity he participated in providing practical solutions for engineering problems ranging from geophysical equipment design for Novilog in Novi Sad to military vehicles and transportation equipment design for LOHR Industrie in Bačka Topola.

Đorđe Dragojević

Damir Krklješ works as a Senior Researcher at the BioSense institute. He received PhD at the University of Novi Sad, Faculty of Technical Sciences, Novi Sad, Serbia, in 2016. He worked as a research and teaching assistant at the Faculty of Technical Sciences, teaching electronics, sensors and actuators and mechatronics. During that time he participated in many national and international projects, where his main research topics involved robotics (sensorized, two degree of freedom gripper. Sensorized robotic foot for ZMP measurement), force sensors (optical and force sensing resistors) and capacitive angular speed and position sensors, based on printed flexible substrates. Starting in 2013 he moved to the industry, where he worked for a few companies, mostly in a design service area for leading worldwide industrial partners. He was engaged both, as an engineer, and as a head of the department. During that period he was involved in a development of industrial products involving high-end LED lightning digitally controlled power converters, sophisticated devices for water pumps monitoring, power converters, etc. Damir Krklješ published over 30 journal and conference papers.

Damir Krklješ

Csaba Petes received his MSc degree in 2017 from the Faculty of Technical Sciences, Department of microprocessor systems and algorithms, University of Novi Sad, Serbia. His Master thesis: Remote control of moving vehicles using DTMF codes and the video signal on large and small distances.
Csaba received his BSc degree at the Faculty of Technical Sciences, Department of Microcomputer Electronics, where he graduated in 2014. Graduation thesis: Development and implementation of program "Paint" on embedded system. After his college, he founded his own company (Sept 2013), the Consulting Agency Starprofit which represented the Güttler GmbH in Serbia and other foreign companies on Serbian market, mostly in agriculture business.
In parallel he worked as a sales manager and as a teacher of the High School "Mihajlo Pupin" and Technical School in Bečej from October 2014 until September 2017. Now he works at the Biosense Institute on Embedded software and robotics systems. He is on more projects (Antares, MIMA, AgroboFood, WaQuMoS) .
In those projects, Csaba Petes take part primarily in the development of analog and digital electronics for read-out system, design PCB layouts, realization PCB prototypes, supporting the overall design of the robot, integration and signal processing.
Contact address: petes.csaba@gmail.com or chaba@biosense.rs

Čaba Peteš

Dr Branimir Bajac (M), researcher at BioSense institute, received the Ph.D. degrees in 2017 at the University of Novi Sad, Faculty of Technology Novi Sad, Serbia, in field of materials engineering. Author and co-author of more than 30 papers in international journals and conferences and 1 book chapter. His PhD involves research in ceramic multiferroic thin films, but his current scientific interests are in fields of metal-oxide gas sensors, biopolymers and optical gas sensors in smart food packaging. Experienced in various material procesing techniques and film deposition, characterization of their structural and functional properties. In the last 5 years a member of two H2020 projects (Shealthy, PROTEIN), two COST projects, one EUREKA, and two national Serbian project.

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Branimir Bajac

Dr Boris Brkić is a Principal Research Fellow at BioSense Institute. He is an electrical engineer with his BEng and PhD obtained from the University of Liverpool. His expertise lies in simulation, design, development and implementation of portable mass spectrometers. Before joining BioSense, Dr Brkić worked as a research associate at University of Liverpool and as a principal project manager at Q Technologies Ltd. In this period, he was involved with research and development of portable membrane inlet mass spectrometers (MIMS) for detection of volatile organic compounds (VOCs) for environmental, security and energy applications. He led 10 research projects in total which included 2 Serbian national projects (Proof-of-concept and Prisma), 2 UK national projects (EPSRC), 4 EU projects (FP7, H2020 and Horizon Europe) and 2 industrial projects funded by British Petroleum, British Gas, Chevron, ConocoPhillips, ENI and Royal DSM. His current research interests are in areas of sustainable agriculture, personalized nutrition, food security and environmental applications.

<a href="https://www.scopus.com/authid/detail.uri?authorId=12545638500">SCOPUS</a>

Boris Brkić

Željko Bajić obtains MSc in Computer science at Faculty of Sciences, University of Novi Sad in 2012. He works as a senior associate at BioSense institute since 2018. His expertise covers full stack technologies mostly based on Java programming language and Web development. He participates in several national and international research projects.

Željko Bajić

Milijana Vujasinović is the head of Product Development Center. She has many years of experience in development and implementation of software solutions. In the development of software solutions, she was initially involved as a programmer and business analyst, and later as a solution designer and product manager. She gained a lot of experience working as an ERP consultant and project manager in implementation of software solutions in different industries – agriculture, wholesale and retail chains, food, textile, metallurgy, tool and cable, construction, service industry, metallurgy and public companies. She has SAP Financial Accounting certificate and SAP Project Manager certificate. For more than 3 years, she managed PMO (Project Management Office), the department that controls and improves the project management process.

Milijana Vujasinović

Ms. Milica Trajkovic (F) is a certified PMI project manager professional (PMP), experienced in EU funded projects focused on Digital Agriculture and development of ICTs for AgriFood value chain. As a Head of Business Development Department, she is responsible for building new and maintaining fruitful partnership network of the Institute both with industry and academia in Serbia and across Europe. Milica manages multi-national and cross-sectoral consortia, supports Institute in new project preparations (from an idea to design and proposal establishment) and brings AgTech market trends closer to the BioSense scientists. One of her goals is to foster entrepreneurial mentality among Institutes’ employees and support them in their own entrepreneurship endeavors. In addition, she facilitates business acceleration for high-tech startups, SMEs and web entrepreneurs from the region with the main aim to bring start-up tech community closer to the AgriFood sector.

Milica Trajković

Maša Mimica works as a Project Manager at BioSense Institute, covering two equally important fields – H2020 financial management and project management of cascade funding projects. She has extensive experience in financial reporting at the project level, portfolio of 17M€ with a proven track-record in planning, reporting and analysis of business needs and other financial aspects of the Institute. Maša as part of Business Development Department collaborates with other cross-finance teams in driving strategic decisions and ongoing operations.
In parallel, Maša acts as a Project Manager for cascade funded sub-projects, previously in FP7 and now in H2020 programme. Thus, she has a vast experience in tailor-made innovation and acceleration activities, which led to market positioning and expansion of more than 150 SMEs, cross-cutting IT and agricultural sectors.
Maša has strong background in multinational environment, where working with different stakeholders across the board in various industries, contributed to the development of her business and interpersonal skills.

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Maša Mimica

Mr. Bojan Gavrilović is from Novi Sad, Serbia. He holds an MSc in Engineering Management from the Faculty of Technical Sciences, University of Novi Sad, Serbia as well as a master’s degree in International Public Procurement Management from the University of Tor Vergata in Rome, Italy.
Bojan has previously worked for private companies in various industries, before becoming part of the ERP implementation team in Apatin Brewery in Serbia. As part of the Change Management team, he became more and more interested in supply chain management.
He later started working in Elektrovojvodina (now part of the DSO), a Serbian utility company in the electric power industry, where he rose through the ranks from a trainee to Head of Public Procurement in the five years he spent there, from where he went on to manage public procurement and strategic IT projects in the parent company of DSO - Electric Power Industry of Serbia.
From 2018, Bojan has been a member of the BioSense Insitute team working on H2020 projects and managing our public procurement operations.

Bojan Gavrilović

Željana Grbović is a young researcher and PhD student in electrical engineering at University of Novi Sad, Faculty of Technical Sciences. She is highly interested in the application of machine learning algorithms and deep learning in the field of agriculture. Her beginnings in machine learning started 2016 when her team ranked in the top 5 among 52 teams in the prestigious competition SP Cup organized by IEEE. She received her master’s degree with the thesis about ear detection in wheat thermal images using deep neural networks. She has expanded the field experiment on the wheat involving images from mobile phones to provide early prediction. She has got funding from Innovation Fund, for The Proof of Concept for her PhD research topic. She is a guest researcher at Wageningen University and Research (WUR) in the group of Computer Vision and Agro-Food Robotics. She was involved in a Humistatus project about the prediction of spreading fungal infection on tomatoes based on hyperspectral images funded by the company Green Yard and WUR. She was a member of the team who tested PerClass MIRA Software for hyperspectral image processing before it was launched as public. Her main interests are image processing and application of transfer deep learning in plant phenotyping, food quality and safety. She is an active member of the artificial intelligence community and was invited speaker on several meetups and conferences where she promoted obtained results on phenotyping. She was a leader of a team that won third prize in the national competition „Space4Women“. Also, she led a team that participated at Alibaba Get Global Challenge final competition. She is actively involved in a few Horizon 2020 projects including ANTARES, Cybele, Dragon (serves as Dissemination Manager) and AgROBOfood and a few industrial projects with partners.

Željana Grbović

Prof. Dr. Vladimir Crnojević is founder of BioSense Institute – R&amp;D institute for information technologies in biosystems, the only European Center of Excellence outside of EU. BioSense has been established in 2015 based on his idea that dates back in 2005 and the vision that information technology and biotechnology will meet in the future. Under his leadership and from only a few enthusiastic individuals BioSense has grown into respectable institution with almost 200 employees and more than 70 PhD holders. BioSense is well known as a very multidisciplinary institute which applies state-of-the-art technology like AI, robotics, nanotechnology and new materials in biosystems – agriculture, environment and medicine. Apart from excellent science BioSense Institute is very much devoted to the industrial cooperation through the creation of innovative solutions and strongly supports innovation development through accelerator activities and spin-off establishment. BioSense has given strong support to the digital transformation of the public sector and society in Serbia tthroug the development of various systems used primarily in agricultural sector.

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He is a professor of computer science at University of Novi Sad, Serbia. He studied Electrical Engineering and obtained his Ph.D. in image processing from the same university in 2004. Since then he has been involved in a research and development in the field of machine learning – nowadays popularly known as artificial intelligence. He was one of the pioneers in this field in Serbia which resulted in a number of new PhDs and various industrial projects. He acted as a director of Power, Electronics and Communications Department at Faculty of technical sciences, University of Novi Sad and served a member of the National scientific board for Electronics, Communications and Information Technology. He is a member of the National council for artificial intelligence of Republic of Serbia and Council of Europe  Committee on Artificial Intelligence.

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Prof. Dr. Crnojević is a part of the 10-Member-Group, prestigious group of experts acting as consultants to the Secretary General of United Nations in the filed of science, technology and innovation for Sustainable Development Goals (UN SDG). He has published more than 100 papers in renowned journals and conferences and acted as a reviewer for some of the most eminent journals in the field. His current interests are related to the role of AI in the achievement of UN SDGs and the future of humanity.

Vladimir Crnojević

Sanja Brdar is a senior researcher and head of the Centre for Information Technologies at BioSense Institute. She received a PhD degree from the University of Novi Sad in 2016. Prior to starting her PhD studies, she spent two years working in the software industry with a major in databases design and development. In 2010 she was awarded a ten-month Basileus fellowship for joining the Bioinformatics Laboratory at University of Ljubljana which set the foundations for the PhD thesis on data/knowledge fusion in bioinformatics. Currently, she is actively involved in many projects including H2020 Antares, Dragon, Bestmap and Flexigrobots and teaches two master level courses at Faculty of Sciences. Her research interests include machine learning, explainable AI and bioinformatics. She works on ensemble methods, data fusion, clustering, and predictive modelling with applications in biology, agriculture, environmental sciences and health. She enjoys participating in worldwide data science challenges and with the team of researchers made significant results: placed third in Nokia Mobile Data Challenge (2012), finalist of Orange, France, Data for Development Challenge (2013), finalist/winner/third place of Syngenta Crop Challenge (2016, 2017, 2019) and finalist of UNDP Depopulation challenge (2020).

Sanja Brdar

Oskar Marko, Ph.D. is the Head of the Center for Information Technologies. His research focuses on applying advanced machine learning and evolutionary algorithms in agriculture. He spent the 3rd year of his undergraduate studies at City University London, where he did his final BEng project in signal processing. He led BioSense’s team that developed novel Big Data algorithms for yield prediction, seed distribution, and optimization of sowing strategies, which secured the 1st prize for BioSense at the Syngenta Crop Challenge and CGIAR Inspire Challenge. He is actively involved in many Horizon 2020 projects including Antares, Cybele, Dragon, and Flexigrobots, and industrial projects with partners such as Krivaja, MK Agriculture, Delta, and other farming and insurance companies.

Oskar Marko

Nikola Obrenović works as a Senior Researcher at the Center for Information Technologies, BioSense Institute, University of Novi Sad. He received his PhD degree in Electrical and Computer Engineering – Computing and Control Engineering from the University of Novi Sad, in October 2015. His doctoral studies and thesis incorporated areas of model-driven database development, rapid prototyping and automatic design verification. Between July 2017 and June 2019, Nikola was employed as Postdoctoral researcher at École polytechnique fédérale de Lausanne, Switzerland. There, Nikola participated in several EU and Swiss national projects in the fields of mathematical optimization and operations research. Before joining BioSense Institute in July 2019, Nikola held a position of an Assistant Professor at the Department of Power, Electronic and Telecommunication Engineering, Faculty of Technical Sciences, University of Novi Sad. Currently, his research interests include mathematical optimization, operations research, machine learning, databases, and model-driven software engineering.
Additionally, Nikola Obrenović has strong practical experience in software industry. He has been employed as a software engineer and architect at several international software companies and independent projects. Most notably, he was a software architect for project “JourneyTree – Advanced Customer Analytics Made Simple”, funded by DataPitch program, under the European Commission’s Horizon 2020 research and innovation program.

Nikola Obrenović

Miloš Pandžić is a Junior Research Assistant whose interest includes several research fields: Earth observation, remote sensing, image processing and data analytics. He is currently enrolled as a PhD student of Geodesy and Geoinformatics at the Faculty of Civil Engineering, University of Belgrade, where he also obtained his master and bachelor degree. In 2015 he received scholarship from the Government of Austria (OeAD) and spent 4 months at TU Vienna where he analysed geometric quality of Sentinel-2 satellite multispectral images. Outcome of this work was his master thesis. After graduation he worked at the Finnish Geospatial Research Institute (FGI) on several projects funded by Academy of Finland and European Space Agency (ESA). His main occupation was segmentation and change detection in satellite imagery related to agricultural activity, urbanization and deforestation. In 2018 he joined BioSense Institute where he works on data fusion of optical and radar images for yield prediction, crop classification, crop monitoring, etc. He is involved in several national and international projects including Antares, Cybele, Dragon, PARSEC, agROBOfood.

Miloš Pandžić

Dr. Marko Panić is a research associate in the field of technical sciences and information technologies at the BioSense Institute. His doctoral dissertation is related to the development of algorithms for accelerated image reconstruction from incomplete measurements using a sparse representation of signals and statistical Markov random models. His research direction includes statistical modeling of multidimensional data obtained from different sensors. He is currently working on the analysis and processing of images obtained from hyperspectral and multispectral cameras using advanced machine learning methods with application in assessing the quality of fruits and vegetables. As a member of the BioSense team, he participated in Syngenta's "Crop Challenge" competition, which resulted in winning first place. He is actively involved in many projects from the Horizon 2020 call, including Antares, Cybele, Dragon and Flexirobots, as well as in projects with companies such as Krivaja, MK Agri and Delta Agrar.

Marko Panić

Gordan Mimić is a Senior Research Associate at BioSense Institute. Gordan Mimić received BSc (2011), MSc (2012) and PhD (2016) degrees in Physics-Meteorology at the University of Novi Sad, Faculty of Sciences. He was working at Republic Hydrometeorological Service of Serbia during the period 2011-2012, in the division for Numerical Weather Prediction. Gordan Mimić was teaching assistant for the various courses in meteorology at the University of Novi Sad (Faculty of Sciences and Faculty of Agriculture) in the period 2012-2017. His current research interest is predictive analytics in the field of agrometeorology. He was the leader of the BioSense team that developed a novel algorithm for the classification of maize hybrids tolerant to drought and heat stress, which won the third prize at Syngenta Crop Challenge in Analytics 2019. He participated in several national and international projects. Gordan Mimić has co-authored 33 journal articles, 26 conference papers, 3 technical solutions and 1 book chapter.

Gordan Mimić

Branislav Pejak is a PhD student of Electrical Engineering at the Faculty of Technical Sciences, the University of Novi Sad, where he also received his BSc (2017) and MSc (2018) in the field of Biomedical Engineering. During his bachelor and master studies, he held a scholarship from the Ministry of Education, Science and Technological Development, Republic of Serbia. Since March 2018, Branislav has been working as a Junior Research Assistant at the Center for Information Technologies, BioSense Institute. His main research is data analytics on satellite images. Currently he is working on crop classification and crop yield prediction using time series of multispectral images and GAN machine learning framework. Within the project he is also working on many practical usages of ICT in agriculture such as smart soil sampling and fertilization, irrigation zone mapping, grain yield and protein content prediction etc. He is actively involved in many Horizon 2020 projects including Antares, Cybele, Dragon and AgRoboFood.

Branislav Pejak

Andrea Simeon obtained a BSc and a MSc degree in Applied Mathematics at the Faculty of Sciences in Novi Sad, Department of Mathematics and Informatics, in 2018 and 2020 respectively. Her undergraduate studies were mainly oriented towards the finances, but she changes the course of her education and enters the master program in English, Applied mathematics – Data Science. During the studies she participated with a fellow in the medic challenge organized by Philip Morris Company “Metagenomics diagnosis for inflammatory bowel disease challenge” and they ranked 7th. This challenge was an inspiration for her to continue working on similar problems. In 2020 she enters PhD studies in Informatics at the Faculty of Sciences in Novi Sad. Currently, she works as a Junior Research Assistant at the BioSense Institute with main research interests in the analysis of microbiome data and exploration of statistical and machine learning techniques in microbiome studies.

Andrea Simeon

Tijana Nikolić is senior researcher at BioSense Institute with a PhD in ecology from the Faculty of Science, University of Novi Sad. She works with spatial prioritization of natural and semi natural habitat in agricultural landscapes, needed to protect agro biodiversity, improve soil quality and secure food production. The main focus is on integrated landscape management that entail food production, biodiversity and climate change across sectors and stakeholders to meet the range of short and long term societal needs.

Tijana Nikolić Lugonja

Dr Branko Sikoparija is a Principal Research Fellow at the Center for Biosystems and quality manager (ISO17025) in the Laboratory for Palynology University of Novi Sad Faculty of Sciences. His research focus is aerobiology and currently, he is interested in developing real-time detection and identification of bioaerosols. Branko Sikoparija is an accomplished writer and reviewer for multiple scientific research journals. Serves as Associate Editor for Aerobiologia. He coordinates the RealForAll network of automated bioaerosol monitors on the Pannonian plain. He is also an active member of the EUMETNET AutoPollen program, vice chair of the COST Action CA18226, the chair of the European Aerobiology Society Working Group for Quality Control, and principal investigator in the BREATHE project financed in the PROMIS call Science Fund of the Republic of Serbia.

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Branko Šikoparija

ANTARES

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This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 739570. 
 


Horizon 2020

With a rapidly growing global population, food systems face increasing pressure. At the same time, diet-related non-communicable diseases and rising consumer demand for healthier food products are accelerating food reformulation and innovation.

We develop, optimize and implement new technologies like 3D food printing, as a controlled platform to study the relationship between food composition, structure and food-microbe-host interactions, and deliver nutritionally and sensorially customized food prototypes for a healthy and sustainable diet. Using sustainable functional ingredients and alternative proteins, derived from insects and other underutilized bioresources, we investigate their bioactive potential to identify compounds with a potential for health improvement.

In close collaboration with the Center for Sensing Technologies and Center for Information Technologies, we integrate data-driven food formulation design and process optimization with physiologically relevant microfluidic in vitro validation systems for evidence-based functional food research and innovation.

Food Innovation and Health

Launch your AgTech idea!

BioSense Accelerator is an intensive 3-month program that targets early-stage AgTech startups and acts as a bridge between science and business. It is an excellent opportunity for startups to bring their bold AgTech ideas to life, grow, fast track their journey, and solve key challenges in the agrifood sector. The accelerator is the only one in the country that provides support to domestic and regional IT entrepreneurs focused on creating innovative and groundbreaking digital solutions for agriculture.
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The first batch of BioSense Accelerator started in October 2019 and over the course of three months that were jam-packed with interactive workshops and bespoke trainings, 8 teams mastered multiple skills: how to develop a business idea, define a business model, identify the needs of future users and conduct detailed analysis of the market and competition. Top-notch tech and business support, as well as guidance from industry and technology experts brought startups and their ideas to the next level. In the second batch (2020), we are joining forces with EFSE Fund and the best B2B accelerator in Europe, Startup Wise Guys.
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Besides a comprehensive support, BioSense Accelerator has given the teams a get-ready guide for fundraising, and startups that participated in the first batch raised 140K Euros over a period of six months.
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The idea of BioSense Accelerator is to boost entrepreneurial mentality in our country and subsequently, that will lead to economic growth and development. The goal is also to motivate regional IT entrepreneurs to be bold and create their own products.
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Find out more: <a href="http://accelerator.biosense.rs/">BioSense Accelerator</a>
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BioSense Accelerator

BioSense’s research in the domain of imaging technologies relies on different devices for acquisition of data. They vary in the parts of the spectrum they cover and the number of bands in which they operate. These devices include visible near infrared (VNIR) and near infrared (NIR) hyperspectral cameras for lab research, and multispectral and thermal cameras which can be also used for the in-field purposes.

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Laboratory analysis is more accurate as hyperspectral cameras perform line scanning in more than 200 spectral bands and to further increase the reliability of measurements, they need to be calibrated using the reference sheets. However, high accuracy requires a large storage and very efficient image processing algorithms, since a hyperspectral image can take up more than 2 GB of space. For reduction of dimensionality we are selecting the most discriminative spectral bands, bands with high signal-to-noise ratio or applying the standard techniques such as PCA and ICA. Thermal and multispectral images also require pre-processing, but it is more focused on noise reduction using filtration. In the next step, depending on the problem, we are relying on feature engineering, clustering techniques and deep learning to segment the image into the regions of interest. Afterwards, depending on the problem, we are using the techniques of post-processing for final representation of results, or we are feeding the results into the machine learning models for higher-level analysis.

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When it comes to applications, we used these advanced cameras to develop algorithms for detection of fungal infections in vegetables and for revealing bruises in fruit days before visible symptoms occur, but they have also proven extremely valuable in ecosystems monitoring and yield prediction. These algorithms allowed us to get more information about the plants, estimate shelf-life of agricultural products, predict the crop yield and provide objective recommendations in post-harvest decision-making.

Hyperspectral, Multispectral and Thermal Imaging

With the advancement of variable rate technologies (VRT) used for sowing, watering, fertilizing, pesticides and herbicides application, the need for systems that can precisely and quickly determine soil variability and identify management zones within a single plot emerged.

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We address this complex task using robot platform Agrobot Lala, which is specially adapted for agricultural applications as well as for incorporating sensors for soil analysis and soil sampling systems. The proposed system moves through the field and perform soil sampling and soil parameters analysis as well as the georeferencing the results of the analysis. The complete process is autonomous since it is possible to predefine the route of Agrobot Lala on the plot using GPS coordinates and a 3D Real-Time-LIDAR system.

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As a result of the analysis, multilayer maps are generated, which can be used to make the right decisions, optimize the production and make full use of the precision agriculture in terms of efficiency, yield increase and sustainability of agricultural production.

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Projects:

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agROBOfood: Business-Oriented Support to the European Robotics and Agri-food Sector, towards a network of Digital Innovation Hubs in Robotics, H2020-DT-2018-1

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Development of the device for measurement and mapping of nitrogen as the most important parameter in sustainable agriculture, contract no. 114-451-2794/2016-03, funded by Provincial Secretariat for Higher Education and Scientific Research of Autonomous Province of Vojvodina, Republic of Serbia.

Agrobot for in-field soil analysis

Agricultural robotics is a field that is becoming more and more mature, with new solutions constantly reaching the market. This area has seen a significant growth due to the shortage in workforce, high costs of labour and tediousness and repetitiveness of the tasks. BioSense uses robots as moving platforms for gathering the data about the crop production. These platforms are equipped with both commercially available sensors and sensors developed by the Group for Sensor Design.

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The latest project we have been working on is concerned with soil in-field analysis. The proposed system moves through the field and perform soil sampling and soil parameters analysis (macronutrients, micronutrients, pH, EC) as well as georeferencing of the results of the analysis. The complete process is autonomous since it is possible to predefine the route of robotic platform on the plot using GPS coordinates and a 3D Real-Time-LIDAR system for reconstructing the surrounding area and avoiding any possible obstacles. As a result of the analysis, multilayer maps are generated where each layer represents one of the parameters obtained by the proposed system. Based on these maps, the farmer will be able to make the right decisions, optimize the production and make full use of the precision agriculture in terms of efficiency, yield increase and sustainability of agricultural production.

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Robotics

At Biosense Institute we are using quadcopter DJI Inspire 1 to gain better insight into crop production and plan and manage agricultural operations more accurately. The technology can collect very high-resolution imagery below cloud level, with much more detail than the satellite imagery. The datasets include images captured with RGB and modified NDVI camera, but we are also using Flir thermal camera to make temperature measurements over large areas of heterogeneous terrain. Unmanned aerial vehicles are easy to use, as most mapping and data-collection missions are now conducted autonomously, by feeding the shape file of the desired area into the dedicated software.

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For image stitching we use Pix4D and AgisoftPhotoscan software to generate high-resolution orthomosaics, vegetation index maps and DSMs (Digital Surface Models). These orthomaps are important layers of information and they can be integrated into GIS systems along with other layers derived from additional sources. These datasets are then processed and used for training of advanced machine learning algorithms.

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BioSense uses UAS (Unmanned Aerial Systems) to detect nutrient deficiencies, pest infestations, water stress or excess and other conditions that affect crop development and improve the management of crops. They are especially useful for fields of a few dozens of hectares, such as orchards or vineyards, as scanning large fields of arable crops requires a lot of time. UAVs have also proven as effective tools for environmental monitoring, as they can cover medium-sized habitats and tell us more about the type of surroundings that insects and mammals grow in. The use of UAVs revolutionized spatial ecology and agriculture and allowed us to get high-resolution data and make optimal decisions with high accuracy.

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NDVI map of sugar beet crop

Drone Imaging

BioSense is the location of a Sentinel Data Hub and we are an active user of products from the ESA’s Copernicus programme. Before the launch of Sentinel satellites, our research was based on Landsat 7 and 8 and RapidView images, but today, it is mainly focused on data coming from Sentinel 1 and 2 missions. Images from Sentinel 2 are coming in 13 spectral bands, approximately every 5 days, depending on the cloud coverage, while Sentinel 1’s synthetic aperture radar allows for monitoring even in the case of clouds. This ensures continuous high-resolution monitoring of the environment and agricultural production in particular, throughout the growing season.

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Since 2012, we have been producing land-cover maps for the Serbian province of Vojvodina, which distinguish fields of different crops, urban areas, forests and water bodies. This allowed us to map animal habitats, observe their change and capture the trends in crop rotation. Using the principles of data fusion, we reached the sub-pixel resolution using data sources with heterogeneous pixel grids. The second line of research is concerned with yield prediction. Based on the ground truth coming from field records, crowdsourced data and yield maps, we are training advanced machine learning models to estimate the yield. These models are based on more than 30 satellite vegetation indices, that incorporate the domain knowledge about the crops, and their accuracy is increased as plants progress through phenological stages. Vegetation indices are also excellent proxies for the damage assessment, as some of them are excellent proxies for damage caused by hail, drought or fire. This puts insurance companies among our partners, along with the government, research institutions and, nevertheless, the farmers, who are using our results to optimise the decisions concerning the time and amount of irrigation and distribution of fertilisers and pesticides.

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Satellite imaging

European Space Agency (ESA) has launched a constellation of Sentinel satellites through Copernicus programme. With €7 bn investment, it is the most ambitious programme of Earth observation in the history of space exploration and the satellite data have found wide application in climate change monitoring, civil engineering, and emergency response during extreme weather events. Data generated through this project are completely free and open for everyone. It is also believed that the European economy will generate €30 bn by 2030 through companies based on satellite data analytics. Due to the huge boost that Copernicus will give to high-tech start-ups and companies, people are often talking about the “Copernicus economy”.

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In 2017 Serbia has become the only country that as an associated member of the EU has a wideband access to ESA’s images. “Sentinel Data Hub” founded at BioSense Institute receives the data from satellites and these data are processed on high-performance servers. For the dissemination of satellite images, DHuS (Data Hub Software), a Java-based software, is used. It is an open-source software that enables user-friendly web environment for interactive discovery and download of data, as well as an excellent application programming interface (API), that allows users to access the data through their programs, scripts and client applications. In this way, the download is automated and the imagery became available to everyone around the world.

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ESA’s images have been widely used at the Institute for development of crop classification maps, yield prediction and growth monitoring using numerous vegetation indices, with Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Soil-adjusted Vegetation Index (SAVI) being the most popular ones. We are expecting that Serbian companies will also use this opportunity to develop industrial solutions based on Sentinel data and come out to the global market with their innovations.

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Sentinel Data Hub

Big data analytics is based on heterogeneous data sources coming at different velocity and at a different resolution. Besides satellite images, BioSense’s AI group uses probes for soil electrical conductivity, handheld devices and information coming from agricultural machinery. Electrical conductivity of the soil is an excellent indicator of soil texture, compaction and chemical content and these parameters are scanned at various depths, depending on the setup and the plants of interest. Elevation maps are not less important, as they determine the flow of nutrients and often correspond to the yield, while the handheld devices, such as the BioSense’s Plant-O-Meter, are allowing for continuous growth monitoring.

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<img class="alignnone wp-image-1486" src="https://api.biosens.rs/wp-content/uploads/2021/06/46-212x300.jpg" alt="" width="220" height="311" /><img class="alignnone wp-image-2435" src="https://api.biosens.rs/wp-content/uploads/2021/06/prostorna-212x300.jpg" alt="" width="220" height="311" />

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Using the techniques of data fusion, heterogeneous data are aggregated into a single dataset and used for the delineation of management zones. Zone delineation is done by applying clusterization algorithms on data, either in normalised or weighted form, depending on the relative importance of input features. These management zones have different properties between one another, but soil can be considered homogeneous within each of them. This allows for issuing recommendations concerning agricultural production separately for each zone, depending on its specifics. Such localised decision-making is the essence of the precision agriculture and it is also a necessary first step in determining the optimal sensor locations, which maximise their effectiveness.

Geospatial data analytics

With the occurrence of non-parametric machine learning models and especially with the rising popularity of deep neural networks, most of classifiers and regressors used today are essentially black boxes. Their accuracy is often unmatchable by any conventional model, and although we know the principles and mathematics behind them, we cannot directly infer the complex relationships between input and output features. Having an accurate model has a huge practical value, but in order to have richer knowledge about the problem and push the boundaries of science, we need to delve deeper into the black box.

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There are a few techniques we are relying on in knowledge discovery and all of them are based on indirect observations of the machine learning models. One of them is the random permutation test, which estimates the feature importance based on the decrease in model accuracy in case of random permutation of its values in the dataset. Morris’s and Sobol’s uncertainty and sensitivity analyses on the other hand are telling us more about the possible outputs of the system and the interaction between the input parameters. Model explanation analysis even goes a step further. For each feature used in predictive model, through sampling-based approximation it gives the estimate of feature’s positive or negative contribution to the output, which enables us to direct the domain researcher to where it’s the most effective. These and other methods allowed us to determine the influence of various soil and weather parameters on the yield and to characterise drought resistant maize hybrids or varieties of soya with stable yield.

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Knowledge discovery

The goal of BioSense’s AI Group is to get a comprehensive and accurate overview of biosystems, make phenotyping and plant breeding more efficient, understand molecular biology better and offer optimal and automatic decision-making to the farmer. Our research starts with the problem and requires innovative application of state-of-the-art algorithms and development of the novel ones. Machine learning algorithms extensively used in BioSense applications include Random Forest, SVM, Decision Trees, Ridge Regression, Spectral Clustering and many others including special family of machine learning algorithms- Deep Learning, and where the complexity of the problem goes beyond the available algorithms, BioSense researchers develop the novel ones.

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Example of novel algorithm is include weighted histograms regression (WHR). WHR is based on a voting mechanism, where training samples are voting for the output value of a test sample. The votes are weighted according to the similarity of features and used to form a histogram that essentially represents the PDF of the output, while the expectation of the PDF is essentially the predicted value. This algorithm proved very useful for yield prediction, where it outperformed random forest, SVM and artificial neural networks.

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Another novel algorithm is integrative clustering based on Nonnegative matrix factorization. Combining results of individual clusterings together into an ensemble leverages evidence accumulation in order to improve the results of clustering. Individual clustering results are merged into a joint cluster membership matrix that is further factorized into matrices of encodings and basis. Algorithm can integrate clusterings stemming from different data sets, different data preprocessing steps or different subsamples of features or objects.

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This algorithm proved very useful for functional genomics. Proposed approach based on nonnegative matrix factorization can fuse diverse data sources and infer gene clusters with high functional enrichment and improved gene coverage.

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Machine Learning

Machine learning and domain expertise are often viewed as two competing approaches for solving problems in any area, including agriculture. Our strategy is to use the best from both worlds. Machine learning algorithms show excellent performance in finding hidden dependencies within the data, but there are already well-established theories that explain the natural processes and they have been proven in practice. We are fully exploiting this fact by incorporating the domain knowledge in our models. This is usually done in the process of feature selection and feature engineering.

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One of the examples of feature engineering, based on the domain expertise, was used for the selection of stress-tolerant maize hybrids in Syngenta Crop Challenge in Analytics 2019. Drought is a phenomenon related to longer period of time without precipitation accompanied with high temperatures and low air humidity. However, there is no strict mathematical formulation of drought and many indicators have been used in practice.

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Based on daily values of maximum and minimum temperature, precipitation and vapor pressure in several stages throughout the growing season, we designed features indicating to drought and heat stress at the specific environment. They included maximum number of consecutive dry days, low precipitation sum in a certain growth stage and vapor pressure deficit. In this problem, the engineered features helped us to determine the drought-resistant hybrids that should be planted to maximise the yield and minimise the risk of low income for the farmer, but they can be used in other as well. Other examples include engineering of meteorological features for prediction of airborne pollen concentrations, engineering of elevation features to better predict the flow of nutrients in the field and engineering of features that describe the animal behaviour for prediction of disease and mammary inflammation.

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Feature Engineering

Deep learning is a rather new method of machine learning, that is due to its specifics and excellent performance in image processing often considered as a separate field within artificial intelligence. It works best with big datasets which have multi-dimensional characteristics, and there are a number of such datasets processed at BioSense.

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The first group of datasets are hyperspectral, multispectral and thermal images acquired in the lab or during the outdoor field experiments. Here, the the goal was to segment the images and recognise characteristic patterns within them, such as bruises and scratches on apples, infected tomato sepals or ears in images of wheat. Different architectures were tested with various parameters, which included the number of convolutional and fully connected layers, type of activation functions and position of dropout layers. They all were optimised for a particular problem, concerning the size of input images and the number of samples in the database.

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The second group consists of airborne particle measurements acquired with Rapid-E, the ﬁrst automated particle detector based on laser-induced ﬂuorescence technology, which records scattered light and laser-induced fluorescence patterns, representing the shape and chemical footprint of the particles. This is one of the examples of deep neural networks (DNNs) that relied heavily on convolutional layers, due to their ability to deal with datasets fused from different data sources.

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In both applications, preprocessing is perhaps the most vital task, as the noisy and non-standardised inputs are very difficult to analyse. Also, an important thing to note is that every problem requires a customised solution, whose complexity corresponds to the richness of the dataset and whose accuracy justifies its learning and execution times.

Deep Learning

Biological sciences currently produce a wealth of data, especially on the molecular level. Over the last decade, the growth rate of these data has surpassed Moor’s laws of doubling size at every 18 months, with a recently estimated doubling size at every 7 months. These diverse high-throughput data are often denoted as ’omics’ to collectively characterize different sources - genomics, transcriptomics, proteomics, metabolomics, etc. The plethora of data types and their quantity has induced a fundamental shift in molecular biology research. The field of bioinformatics emerged from the ambition to develop more powerful tools for the analysis and drawing conclusions from omics data.

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The bioinformatics team at BioSense is developing tools and methods for different domains within molecular biology, such as functional genomics, metagenomics, quantitative trait loci (QTL) mapping and genome-wide association studies (GWAS) for traits of significance, mapping of abiotic and biotic stress resistance, etc. To meet the computational and analytical challenges we rely on machine learning, graph algorithms, information theory, data compression and data/knowledge integration. Most of the data we handle falls within the agricultural domain, mainly crop genomics, genome-phenome interactions, genotype-by-environment interactions, but also microbiome data related both to crop and human health.

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Bioinformatics algorithms also have an important role for the emerging technology of DNA-based data storage. Large amounts of generated data call for efficient data storage solutions. DNA-based data storage was proven to possess great potential in this regard, due to its longevity and enormous information density, and is becoming an attractive alternative to the conventional information storage systems. Within this domain our group is focused on the analysis of the fundamental limits of such systems, devising new storage architectures, and developing the corresponding algorithms. Some of the problems we are currently working on are sequence design, error-correction mechanisms, and encoding/decoding algorithms.

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Bioinformatics

The most of BioSense’s research in Big Data is currently concerned with two types of datasets that require huge storage capacity and extremely efficient algorithms that will process them in the specified time requirements.

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The first of them are satellite images. Sentinel 2 satellites are releasing new 13-band images of any given point on Earth approximately every 5 days. These images come in 100x100 km2 tiles, with a 10 m resolution. Within Cybele project, for example, we are analysing satellite images of the whole Europe to classify soya fields, meaning that we need to go through hundreds of tiles and apply the classification algorithm. In such situations, we are parallelising the code to better suit the multi-core server architecture, speeding up its parts using Cython, but also adjusting the implementations to lower the computational complexity of the algorithm.

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Another type of data are call detail records of mobile phone users and they satisfy all the V’s of big data analytics. These records consist of lists of established calls, exchanged text messages and registered internet traffic and are enormous in size. An important issue that needs to be addressed is concerned with privacy and it is a huge challenge to extract useful information from anonymised or aggregated datasets. Also, although the data is semi-structured, the traditional RDBMS technology for data analytics is not applicable to datasets of this volume, so we are developing processing workflows using Apache family of technologies, such as Spark, Hadoop and Hive. They allow us to get a deeper insight into human behaviour and mobility patterns and infer the interaction between rural and urban areas.

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Big Data Analytics

We share an interest in conservation target animal species and their habitats .. We apply our research in natural and semi-natural areas, and work to understand the consequences of human induced environmental change on wild animals.. We focus our research on organisms’ individual, (meta) population and community level. The main goal is to develop joint plans and programmes for local community and nature conservation in agricultural settings.

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Projects:

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Conservation strategy for protected and strictly protected hoverflies (Insecta: Diptera: Syrphidae) species in Serbia - Case study Ministry of Education, Science and Technological Development, Republic of Serbia, Belgrade, 2011-2019

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Evaluation of Ecological Networks in AP Vojvodina as support for nature conservation, Provincial Secretariat for Higher Education and Scientific Research, AP Vojvodina, Novi Sad, 2016-2020

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Spermophilus citellus (Foto - Tijana Nikolić)

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Coracias garrulus (Foto- Dmitrije Radišić)

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Canis lupus (Foto - Institute for Nature Conservation of Serbia)

Endangered animal species

With the aim of timely registration of changes in ecosystems, we direct part of the research towards a group of insect pollinators, hoverflies, which have proven to be very good bioindicators. Their presence and abundance can indicate the levels of disturbance in the ecosystem. Through field expeditions we obtain information on species diversity and distribution. Through the usage of modern technologies, such as drones equipped with different cameras, we obtain information on different parameters related to quality and changes in habitats.

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By combining data on species distribution and those on habitat changes, by using species distribution modeling, analyses of functional composition of communities and spatial analyses, we can estimate how global warming affects the distribution of species and at what level anthropogenic pressures affect the functionality of an ecosystem.

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Set of recommended practices based on these researches can have a positive effect not only on the preservation of ecosystems, but also lead to an increase in yields through the increase in the number of different pollinators in agricultural crops.

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Bioindicators and changes in the environment

ANTARES- Centre of Excellence for Advanced Technologies in Sustainable Agriculture and Food Security

Centar za Biosisteme

Centar za Informacione Tehnologije

Centar za inovacije i razvoj poslovanja

Centar za razvoj proizvoda

Centar za senzorske tehnologije

Cilj projekta: ANTARES ima za cilj da pretvori Institut BioSens u Evropski centar izvrsnosti (CoE) za napredne tehnologije u održivoj poljoprivredi, kao i da pruži vrhunska digitalna rešenja evropskom poljoprivrednom sektoru, podstakne naučnu izvrsnost, stimuliše preduzetništvo i zapošljavanje na regionalnom nivou i obezbedi dovoljno sigurne hrane za rastuću globalnu populaciju.
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O projektu: Da bi se to postiglo, ANTARES će se usredsrediti na tri stuba održivosti: snažnu istraživačku infrastrukturu koja uključuje novu zgradu Instituta BioSens i najsavremeniju opremu, namenski plan ljudskih resursa i naučnu izvrsnost. Kvantni skok u ljudskim resursima i istraživačkoj infrastrukturi koji je omogućio ANTARES povećaće naučne mogućnosti BioSensa. Takoše, Institut će pomerati naučne i tehnološke granice u različitim disciplinama: nauka o materijalima, mikro i nano elektronika, dizajn senzora, daljinsko očitavanje, Internet stvari, veštačka inteligencija i istraživanje biosistema.

 Naučni savetnik u oblasti prirodnih-matematičkih nauka- fizika

Alan je stariji istraživač na BioSense institutu. Doktorirao je hemiju na Sankt Peterburškom državnom univerzitetu (2014). Od 2015. do 2023. godine bio je na poziciji vođe tima u Naučnom odeljenju u biotehnološkoj kompaniji u Sankt Peterburgu, gde je radio na kreiranju ključnih komponenti dijagnostičkih sistema za medicinu. Takođe je učestvovao u industrijskim projektima i dobijao sredstva iz državnih grantova. Trenutno je uključen u projekat ANTARES. Njegov glavni fokus je na stvaranju nano i mikročestica i njihovoj primeni u senzorskim sistemima. Njegovi istraživački interesi takođe uključuju organsku sintezu, biokonjugaciju i imunotestove.

Sonja je završila osnovne i master studije psihologije na Filozofskom fakultetu Univerziteta u Novom Sadu. Njen profesionalni put počeo je 2015. godine kada je započela karijeru kliničkog psihologa u zdravstvenom sektoru. U oblastu upravljanja ljudskim resursima prešla je 2019. godine.

Njene odgovornosti na Institutu BioSens obuhvataju širok spektar funkcija vezanih za ljudske resurse: akvizicija talenata, regrutacija i selekcija, formulisanje i sprovođenje pravila i procedura za kvalitetniji rad i razvoj BioSens-a, razvoj zaposlenih, organizacija treninga za zaposlene, saradnja sa menadžerom za komunikacije sa ciljem promovisanja BioSens-a i zaposlenih na Institutu.

Kristina Kalkan je istraživac-saradnik na Institutu BioSens. Doktorirala je 2021. godine na Prirodno-matematičkom fakultetu, Univerziteta u Novom Sadu na temu kvantifikacije erozivnih procesa zemljišta primenom fallout radionuklida. Njena istraživanja su usmerena na pedologiju (modeliranje erozivnih procesa zemljišta, radioaktivnost zemljišta, magnetna susceptibilnost zemljišta), geomorfologiju, klimatologiju, radioekologiju, radioaerosole, prirodne nepogode i hazarde. Pored navedenog, koristi GIS, daljinsku detekciju i dronove za potrebe prostorne analize i vizualizacije.

Goran Kitić je zaposlen na poziciji Rukovodioca centra za senzorske tehnologije na Institutu BioSens. Doktorirao je na Fakultetu tehničkih nauka, na Univerzitetu u Novom Sadu 2016. godine na temu mikrotalasnih senzora vlažnosti zemljišta. Goran Kitić ima iskustva u oblastima raznih tehnologija fabrikacije (LTCC, debeloslojna i tankoslojna tehnologija, mikrofabrikacija, PCB, wire bonding, mašinska obrada i 3D štampa) i metoda karakterizacije (elektronska mikroskopija, EDS spektoroskopija, UV/Vis spektroskopija, optička profilometrija, električna merenja). Njegova istraživanja su u vezi sa razvojem senzora koji svoju primenu imaju u poljoprivredi, prehrambenom sektoru i zaštiti životne sredine. Neki od razvijenih prototipa senzora uključuju multispektralni senzor za utvrđivanje statusa biljaka, senzor vlažnosti zemljišta, uređaj za rano otkrivanje mastitisa, detektor nitrata u vodi. Pored navedenog, u velikoj meri je uključen u razvoj robotskog sistema za uzorkovanje i analizu zemljišta. Njegovo istraživanje je publikovano u 7 međunarodnih časopisa, i brojnim radovima objavljenim na međunarodnim konferencijama. Tokom svoje karijere, učestvovao je u 13 međunarodnih i nacionalnih projekata.

Vladislava Ćulum se pridružila timu BioSens Instituta 2015. godine. Ima preko 20 godina iskustva u finansijskim i računovodstvenim poslovima. Radila je u privredi i bila je uključena u razvoj strateških planova, planiranje budžeta, finansija i interni kontroling. Bavila se predviđanjem i analizom tržišnih trendova i konkurenata. Nakon završene srednje škole, školovanje nastavlja na Filozofskom fakultetu Univerziteta u Novom Sadu.  Trenutno radi kao finansijsko-računovodstveni analitičar u Finansijskoj službi Instituta.

Gorana Lisjuk zaposlena je kao finansijsko-računovodstveni analitičar za međunarodne projekte u Finansijskoj službi Instituta BioSens. Diplomirala je na Univerzitetu u Novom Sadu, na Ekonomskom fakultetu, smer Evropska ekonomija i biznis. BioSens timu se pridružila 2016. godine kao asistent za inovacije i razvoj poslovanja, gde je stekla iskustvo u pisanju predloga projekata, implementaciji, administraciji i finansijskom upravljanju, pripremi finansijskih izveštaja i rezultata.

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Zoran Pavlović rođen je 1986. godine u Aleksincu, Srbija. Diplomu je stekao 2010. godine, a Master diplomu 2011. godine iz hemijskog inženjerstva na Tehnološko-metalurškom fakultetu Univerziteta u Beogradu. Master teza o elektrohemijskim katalizatorima i razvoju sistema gorivih ćelija sa protočnim ciklonskim reaktorima urađena je na Max Planck Institutu za dinamiku složenih tehničkih sistema, Magdeburg, Nemačka. Doktorirao je 2017. godine na heterogenoj elektrohemijskoj katalizi (dr. Rer. Nat - doktor prirodnih nauka), na Max Planck Institutu za hemijsku konverziju energije, Muelheim an der Ruhr, Nemačka, kao i na Fritz Haber Institutu, Berlin, Nemačka. Tokom doktorskih studija, predstavljao je svoj naucni rad na raznim elektrohemijskim konferencijama u Nemačkoj, kao i na konferencijama Elektrohemijskog društva u SAD-u (Orlando - Florida i San Diego - Kalifornija). Od marta 2018. godine radi u farmaceutskoj kompaniji Phytonet AG, Zurich, Švajcarska, gde je uključen u pokretanje proizvodnog pogona farmaceutskih preparata dodataka ishrani u Novom Sadu. Tamo radi kao menadžer proizvodnje i menadžer tehničkih sistema, gde se bavi optimizacijom postojećih i razvojem novih farmaceutskih procesa proizvodnje i transfera tehnologija, kao i poslovnim razvojem,. U isto vreme, od marta 2018. godine radi kao docent na Farmaceutskom fakultetu Univerziteta u Novom Sadu, gde vodi predavanja iz Industrijske farmacije, Organske, Bioorganske, Stereo i Steroidne hemije, a takođe je i mentor studentima na master i doktorskim tezama, kao i na studentskim praksama u Phytonet-u. Od januara 2021. godine, Zoran radi na Institutu BioSense kao Stariji istraživač, pridružujući se brojnim tekućim evropskim projektima. Njegov glavni istraživački interes je elektrokemijska kataliza u cilju skladištenja energije dobijene obnovljivim izvorima energije, i razvoj bio-elektrohemijskih senzora i materijala za primenu u poljoprivredi, medicini, farmaciji i zaštiti životne sredine.

Prof. Dr Vesna Bengin je suosnivač Instituta BioSens – Istraživačko-razvojnog instituta za informacione tehnologije biosistema, koji se fokusira na multidisciplinarna naučna istraživanja naprednih informaciono-komunikacionih tehnologija i njihove primene u biosistemima, prvenstveno poljoprivredi. Ona je rukovodilac projekta ANTARES, koji je prvorangiran u najprestižnijem pozivu Evropske komisije Horizon 2020 -Teaming, a koji ima za cilj da razvije Institut BioSens u tržišno orijentisan vodeći evropski centar naučne izvrsnosti. Sa budžetom od skoro 30 miliona evra, ANTARES je najveći istraživački projekat koji je u Republici Srbiji ikada finansirala Evropska unija. Pored naučne izvrsnosti, ANTARES projekat se fokusira i na razvoj novih politika koje će podstaći napredak naučno-istraživačke zajednice u Srbiji i ekonomski rast u čitavom regionu.

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Profesorka Bengin je dobitnica najviših priznanja u nastavi i istraživanju: redovni je profesor na Odeljenju za fiziku Univerziteta u Novom Sadu, kao i naučni savetnik (glavni istraživač) kog je izabralo Ministarstvo prosvete, nauke i tehnološkog razvoja Republike Srbije. Takođe ima titulu izvanrednog profesora na Univerzitetu Stellenbosch u Južnoj Africi, kao i poziciju gostujućeg profesora na Univerzitetu u Pensilvaniji (UPENN), SAD. Članica je Upravnog odbora Instituta BioSens od njegovog osnivanja 2015. godine do danas, i potpredsednica istog odbora 2015-2019. Bila je član Upravnog odbora Fonda za inovacije Republike Srbije u periodu 2011-2017 i članica mnogih evropskih i međunarodnih stručnih tela kao što su Generalna skupština Evropske Mikrotalasne asocijacije i Odbor za implementaciju grupe za posmatranje Zemlje (GEO).

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Po pozivu Evropske komisije, radila je i kao mid-term evaluator celokupnog intervladinog okvirnog programa COST (Co-operation in Science and Technology) finansiranog od strane Evropske komisije sa približno 300 miliona evra, u smislu procene ispunjenja političkih, naučnih i operativnih ciljeva programa. Radila je i kao konsultant za Fonds National de la Recherche iz Luksemburga 2017. godine, kao i za niz istraživačkih institucija u Turskoj i Sloveniji 2016. godine. Od 2016. profesorka Bengin je članica UNICEF-ovog poslovnog savetodavnog odbora u Srbiji i konsultant za UN FAO (Ujedinjene nacije - Organizacija za hranu i poljoprivredu).

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Doktorirala je 2006. godine na Univerzitetu u Novom Sadu u oblasti elektronike i mikrotalasne tehnike, autor je preko 100 naučnih radova od čega su 32 objavljena u prestižnim međunarodnim časopisima, jednog nacionalnog patenta, kao i dve knjige objavljene kod prestižnih svetskih izdavača Cambridge University Press i John Wiley &amp; Sons. Vodila je ili učestvovala u 5 projekata iz programa Evropske Unije, Horizont2020, kao i 8 projekata iz programa FP7, uključujući projekat InnoSense kroz koji je oformila laboratoriju za nano i mikroelektroniku koja je i danas jedna od najjačih u ovoj oblasti u regionu i predstavlja osnovnu bazu naučnih istraživanja na Institutu.

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Dobitnica je većeg broja međunarodnih i domaćih nagrada i priznanja, kao što su specijalna nagrada Evropske komisije Marie Curie Actions for an Innovative Europe: Excellence, mobility and skills for researchers, nagrada za izuzetne žene u nauci i tehnologiji She's Mercedes kompanije Mercedes-Benz, Povelja Kapetan Miša Anastasijević za međunarodne naučnoistraživačke projekte i afirmaciju nauke u Srbiji i brojnih drugih.
Majka je troje dece.

Slobodan Birgermajer je naučni saradnik u Centru za Senzorske Tehnologije pri Institutu BioSens. Doktorat iz mikrotalasne elektronike odbranio je 2019. godine na Fakultetu Tehničkoih Nauka, Univerziteta u Novom Sadu. Trenutno radi na razvoju i primeni senzora u poljprivredi, prehrambenoj industriji i monitoringu životne sredine, i aktivno je uključena na više međunarodnih projekata iz poziva H2020, kao što su ANTARES, SHEALTY, IPANEMA, i drugi. Takođe, domen istraživanja obuhvata razvoj i fabrikaciju mikrotalasnih pasivnih kola, fotonskih kristala, mikrofludinih senzora, kao i 3D i 4D tehnologiju štampe.

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Dr Nikolina Janković je stariji Istraživač u Centru za senzorske tehnologije Instituta BioSens. Autor i koautor više od 40 radova u časopisima i na konferencijama, kao i 5 poglavlja u knjigama. Njena polja interesovanja uključuju metamaterijale, akustiku, plazmoniku, senzore i mikrotalasna pasivna kola. Koordinator je H2020 MSCA-RISE projekta NOCTURNO i EUREKA projekat WaQUMoS u Srbiji. In poslednjih sedam godina učestvovala je u 2 FP7 projekta MultiWaveS i InnoSense (menadžer projekta i vođa radnog paketa), kao i u 3 H2020 projekta (H2020 Teaming ANTARES (vođa radnog paketa), NOCTURNO (rukovodilac) i SHealthy).

Georges Dubourg је zaposlen u Institutu BioSens од 16. oktobra 2015. godine. Prethodno je bio zaposlen na Fakultetu tehničkih nauka, na Departmanu za energetiku, elektroniku i telekomunikacije, od septembra 2014. godine kao saradnik na FP7 projektu InnoSense. Od januara 2013. do jula 2014. godine radio je kao posle doktorski istaživač na Institutu za elektroniku, mikro i nanoelektroniku (IEMN) na Univerzitetu u Lilu (Francuska). Doktorske studije završio je na Univerzitetu u Bordou (Francuska), gde je radio kao istraćivač u laboratoriji Integracija: od Materijala do Sistema (IMS). U toku svog dosadašnjeg rada, dr Georges Dubourg se prvenstveno bavio naučnim istaživanjima u oblasti mikroelektronike i mikrofabrikacionih tehnologija, gde je ostvario zapažene rezultate na međunarodnom nivou. Takođe, učestvovao je u izvođenju auditornih i laboratorijskih vežbi kao asistent u nastavi od 2010. do 2012. na Univerzitetu u Bordou (Francuska) na nekoliko predmeta iz oblasti elektronike i tehnologija mikrofabrikacije na osnovnim akademskim i master studijama. Sarađivao je sa studentima pri izradi obaveznih projekata, diplomskih i master završnih radova. Domen istraživanja obuhvataju razvoj i fabrikaciju fleksibilnih senzora, mikrofluidiku i razvoj platformi organ-na-čipu. Georges je koordinator Horizon2020 projekta CISTEM-GA 778354.

Đorđe Vujić je trenutno zaposlen kao Istraživač-pripravnik u Centru za Informacione Tehnologije na Institutu BioSens u Novom Sadu. Diplomirao je na Prirodno-matematičkom fakultetu, na katedri za organsku hemiju. Tema diplomskog rada je bila “Sinteza C-homo laktama derivata žučnih kiselina”. Master studije je završio na Prirodno-matematičkom fakultetu na katedri za organsku hemiju, tema master rada je bila “Ispitivanje sadržaja odabranih monoaromatičnih i hlorovanih ugljovodonika u Dunavu portabilnim masenim spektrometrom”. Posle studija, zaposlio se u laboratoriji u kojoj je bio uključen u analizu kontaminenata hrane kao što su rezidui pesticida, mikotoksini i akrilamid kao i analiza kvaliteta hrane u koju je uključeno određivanje sadržaja šećera, aminokiselina, konzervanasa, veštačkih zaslađivača i vitamina. Trenutno je student doktorskih studija na Prirodno-matematičkom fakultetu u Novom Sadu.

<a href="https://orcid.org/0000-0002-7055-9307">ORCID Profil</a>

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Đorđe Dragojević je trenutno zaposlen kao mlađi istraživač u Centru za Informacione Tehnologije na Biosens institutu , Univerzitet Novi Sad. Diplomirao je na odseku Mašinstvo na Fakultetu Tehničkih Nauka, na katedri za Mehanizaciju i Konstrukciono mašinstvo sa usmerenjem Motori i Vozila sa temom završnog rada „Određivanje granice prevrtanja i provera bočne stabilnosti teških teretnih vozila“. Tokom studija učestvovao je na projektima u okviru Katedre za Biosisteme sa konkretnim učešćem u dizajnu poljoprivredne mehanizacije i opreme. U profesionalnom domenu, tokom više od 8 godina Đorđe je učestvovao na domaćim i međunarodnim projektima u građevinskoj i auto-industriji kao mašinski inženjer-konstruktor. Vođen radoznalošću doprineo je iznalaženju praktičnih rešenja inženjerskim problemima, dijapazona od dizajna merne opreme za geofizička istraživanja u okviru kompanije Novilog Novi Sad do dizajna opreme vojnih vozila i transportne opreme za potrebe vojske u okviru kompanije LOHR Industrie iz Bačke Topole.

Damir Krklješ radi kao viši istraživač na institutu BioSense. Doktorirao je na Univerzitetu u Novom Sadu, Fakultet tehničkih nauka, Novi Sad, Srbija, 2016. Radio je kao istraživač i asistent u nastavi na Fakultetu tehničkih nauka, predajući elektroniku, senzore i aktuatore i mehatroniku. Za to vreme učestvovao je u mnogim nacionalnim i međunarodnim projektima, gde su mu glavne teme istraživanja bile robotika (senzorisana hvataljka sa dva stepena slobode. Senzorisano robotsko stopalo za ZMP-metenja), senzori sile (optički i otpornici osetljivi na silu) i kapacitivni senzori ugla i ugaone brzine, na bazi štampane elektronike na fleksibilnim podlogama. Počevši od 2013. prelazi u industriju, gde je radio za nekoliko kompanija, uglavnom u oblasti dizajnerskih usluga za vodeće svetske industrijske partnere. Bio je angažovan i kao inženjer i kao šef odeljenja. U tom periodu bio je uključen u razvoj industrijskih proizvoda koji uključuju vrhunske LED rasvete, digitalno kontrolisane pretvarače snage, sofisticirane uređaje za nadzor pumpi, pretvarače snage itd. Od 2019 radi na BioSens institutu. Damir Krklješ objavio je preko 30 časopisa i konferencijskih radova.

Csaba Petes je 2017. godine završio Master akademske studije na Univerzitetu u Novom Sadu, na Fakultetu tehničkih nauka, smer mikroprocesorski sistemi i algoritmi. Tema njegovog maser rada je bilo daljinsko upravljanje pokretnih vozila pomoću DTMF kodova i video signala na velike i male daljine. Osnovne akademske studije završio je 2014. godine na Univerzitetu u Novom Sadu, na Fakultetu tehničkih nauka, smer mikroračunarska elektronika, sa temom diplomskog rada : Razvoj i primena programa „Paint“ na embeded sistemu.
U među vremenu (septembar 2013) osnovao je sopstvenu kompaniju, konsultantsku agenciju „Starprofit“ koja je zastupala „Guttler GmbH“ u Srbiji i druge strane kompanije na srpskom tržištu, uglavnom u poljoprivrednom poslovanju. Paralelno radi kao menadžer prodaje i kao profesor stručnih predmeta u Elektrotehničkoj skoli „Mihajlo Pupin“ u Novom Sadu i u Tehničkoj školi u Bečeju od oktobra 2014. do septembra 2017. godine.
Od maja 2018. godine radi na Institutu Biosense, na embeded softveru i robotskim sistemima. Radi na više projekata (Antares, MIMA, AgroboFood, WaQuMoS), prvenstveno na razvoju analogne i digitalne elektronike za sistem očitavanja, na dizajniranju izgleda PCB-a, realizaciji prototipa PCB-a, podržavajući celokupan dizajn robota, integraciju i obradu signala. Kontakt adresa: petes.csaba@gmail.com ili chaba@biosense.rs

Dr Branimir Bajac (M), stariji istraživač na Institutu Biosens, doktorirao je 2017. godine na Univerzitetu u Novom Sadu, Tehnološki fakultet Novi Sad, Srbija, u oblasti inženjerstva materijala. Autor i koautor više od 30 radova u međunarodnim časopisima i konferencijama i jednog poglavlja o knjizi. Njegov doktorat obuhvata istraživanje keramičkih multiferoičnih tankih filmova, dok mu se trenutna naučna interesovanja nalaze u oblasti metal-oksidnih gasnih senzora, biopolimera i optičkih senzora gasova kod pametne ambalaže za hranu. Poseduje široko iskustvo u raznim tehnikama procesiranja materijala i depoziciji filmova, karakterizaciji njihovih strukturnih i funkcionalnih svojstava. U poslednjih 5 godina član dva H2020 projekta (SHealthy, PROTEIN), dva COST projekta, jednog EUREKA i dva nacionalna projekta republičkog ministarstva.

Dr Boris Brkić je naučni savetnik na Institutu Biosens. On je inženjer elektrotehnike sa osnovnim i doktorskim studijama završenim na Univerzitetu u Liverpulu. Njegova stručnost pokriva simulacije, dizajn, razvoj i implementaciju portabilnih masenih spektrometara. Pre dolaska u Biosens, dr Brkić je radio kao naučni saradnik na Univerzitetu u Liverpulu i kao glavni projektni menadžer u Q Technologies Ltd. U ovom periodu bio je uključen u istraživanje i razvoj portabilnih masenih spektrometara sa membranom (MIMS) za detektovanje lako isparljivih organska jedinjenja (VOC) sa primenom u oblastima zaštite životne sredine, javne bezbednosti i energetike. Vodio je ukupno 10 istraživačkih projekata koji su uključivali 2 srpska nacionalna projekta (Dokaz koncepta i Prizma), 2 nacionalna projekta u Velikoj Britaniji (EPSRC), 4 EU projekta (FP7, H2020 i Horizon Europe) i 2 industrijska projekta koja su finansirali British Petroleum, British Gas, Chevron, ConoccoPhillips, ENI i Royal DSM. Njegova trenutna istraživačka interesovanja su iz oblasti održive poljoprivrede, personalizovane ishrane, bezbednosti hrane i zaštite životne sredine.

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<a href="https://www.scopus.com/authid/detail.uri?authorId=12545638500">SCOPUS</a>

Željko Bajić je masterirao 2012. godine na Prirodno matematičkom fakultetu u Novom Sadu. Na Biosense institutu radi kao stariji saradnik. Bavi se programiranjem na svim nivoima uglavnom bazirano na Java programskom jeziku i raznim web tehnologijama. Aktivno učestvuje na nekoliko nacionalnih i međunarodnih projekata.

Milijana Vujasinović je rukovodilac Centra za razvoj proizvoda. Ima višegodišnje iskustvo u razvoju i implementaciju softverskih rešenje. U razvoju softverskih rešenja učestvovala je u početku kao programer i biznis analitičar, a kasnije i kao projektant rešenja i menadžer proizvoda. Bogato iskustvo je stekla radeći kao ERP konsultant i menadžer projekata u implementacijama softverskih rešenja u različitim privrednim granama – poljoprivrednoj,veleprodaji i maloprodaji, prehrambenoj industriju, tekstilnoj industriju, metalurgiji, industriji alata i kablova, građevinskoj industriju, uzlužnoj delatnost kao i u javnim preduzećima. Sertifikovani je SAP FI i SAP Project Manager. Više od 3 godine je upravljala PMO (Project Managment Office), odeljenjem koji kontroliše i unapređuje proces upravljanje projektima.

Milica Trajković je sertifikovani projektni menadžer (Project Management Professional – PMP) od strane prestižnog Instituta za projektni menadžment (Project Management Institute), sa iskustvom u projektima finansiranim od strane Evropske Komisije u oblasti digitalne poljoprivrede i razvoja IKT sektora za poljoprivredu i industriju hrane.
Kao rukovodilac Centra za Inovacije i razvoj poslovanja, ona je odgovorna za izgradnju novih i održavanje uspešnih partnerskih veza kako sa partnerima iz industrije, tako i sa akademskom zajednicom u Srbiji i Evropi. Milica rukovodi multinacionalnim projektima koji obuhvataju nekoliko različitih industrijskih sektora; rukovodi razvojem i pisanjem novih projekata (od ideje do pisanja i realizacije projektnih aktivnosti) i pruža informacije o trendovima na AgTech tržištu istraživačima Instituta. Jedan od njenih ciljeva je snažna podrška preduzetničkim poduhvatima naučnika Instituta. Uz to, Milica podstiče razvoj visokotehnoloških stratapova, MSPP sektora iz Srbije i regiona sa osnovnim ciljem da približi tehničko-tehnološku scenu regiona poljoprovredi i industriji hrane.

Maša Mimica radi kao projektni menadžer u Institutu Biosens na poslovima H2020 finansijskog menadžement ali i kao projektni menadžer podprojekata odabranih od strane Instituta. Ima bogato iskustvo iz finansijskog izveštavanja na projektnom nivou sa potfoliom od 17M€ i sa dokazanim iskustvom u planiranju, izveštavanju i analizi poslovnih potreba i drugim finansijskim aspektima Instituta. Maša kao deo Centra za inovacije i razvoj poslovanja sarađuje sa drugim odeljenjima u donošenju strateških odluka i rešavanju operativnih izazova
Paralelno s tim, Maša radi kao menadžer podprojekata finansiranih kaskadno, ranije u FP7, a sada u programu H2020. Kroz ovu vrstu poslova, stekla je veliko iskustvo u programima inovacija i akceleracije malih i srednjih preduzeća. Kroz projekte na kojima radi došlo je do pozicioniranja na tržištu i rasta više od 150 MSP, kako IT tako i poljoprivrednog sektora.
Maša ima iskustvo rada u multinacionalnom okruženju, gde je rad sa različitim stranama u raznim industrijama doprineo razvoju njenih poslovnih i međuljudskih veština.

Bojan Gavrilović je deo tima Centra za inovacije i razvoj poslovanja. Master diplomu iz inženjerskog menadžmenta stekao je na Fakultetu tehničkih nauka Univerziteta u Novom Sadu, a master diplomu iz upravljanja međunarodnim javnim nabavkama na Univerzitetu Tor Vergata iz Rima.
Nakon rada u više kompanija u privatnom sektoru u različitim privrednim granama, postao je član tima za implementaciju ERP softvera u Apatinskoj pivari, gde je kao deo tima za upravljanje promenama u ovoj kompaniji razvio je interesovanje za upravljanje lancem snabdevanja.
Karijeru je nastavio u Elektrovojvodini iz Novog Sada (danas deo Operatora distributivnog Sistema EPS Distribucija Beograd), gde je napredovao od pripravnika do rukovodioca za javne nabavke tokom pet godina koje je proveo u ovoj kompaniji. Nakon toga, rukovodio je poslovima javnih nabavki I strateškim IT projektima u centrali kompanije, Javnom preduzeću Elektroprivreda Srbije u Beogradu.
Od 2018. godine Bojan je deo tima Instituta BioSens gde radi na međunarodnim projektima i obavlja poslove službenika za javne nabavke.

Željana Grbović je istraživač i student doktorskih studija na departmanu Energetika, elektronika i telekomunikacije na Fakultetu tehničkih nauka, Univerzitet u Novom Sadu. Njena oblast interesovanja obuhvata primenu mašinskog učenja u poljoprivredi. Algoritmima veštačke inteligencije je počela da se bavi 2016. godine kada je njen tim rangiran u TOP5 u konkurenciji od 52 tima na prestižnom takmičenju u obradi signala, SP Cup koji organizuje IEEE. Završila je master studije 2017. godine na temi detektovanja klasova pšenice u termalnim slikama korišćenjem dubokih konvolucionih neuronskih mreža. Istraživanje o pšenici je proširila i na ranu predikciju prinosa, na osnovu slika sa mobilnog telefona i drona, za šta je dobila finansiranje od Inovacionog Fonda, za Dokaz Koncepta. Gostujući je istraživač na Univerzitetu u Vageningenu (Wageningen University and Research (WUR)), u grupi za Kompijutersku viziju i robotiku u poljoprivredi. Učestvovala je u industrijskom projektu WUR-a i kompanije GreenYard, pod nazivom Humistatus, za predikciju širenja patogenih oboljenja na paradajizu, na osnovu hiperspektralnih slika. Glavni domen istraživanja je transfer dubokog učenja u oblasti procene prinosa, kvaliteta i bezbednosti useva u poljoprivredi. Predvodila je tim koji je osvojio nagradu na studentskom univerzitetskom takmičenju „Space4Women“ i koji je učestvovao u finalu svetskog „Alibaba Get Global Challenge“ takmičenja. Aktivni učesnik je na nekoliko Horizon 2020 projekata, uključujući ANTARES, Cybele, Dragon (Menadžer diseminacije) i AgROBOfood, kao i projekte sa kompanijama.

Prof. dr Vladimir Crnojević je osnivač Instituta BioSens – istraživačko-razvojnog instituta za informacione tehnologije biosistema, jedinog Evropskog Centra Izvrsnosti van EU. BioSens je osnovan 2015. na njegovoj ideji iz 2005. godine i viziji o spajanju informacionih tehnologija i biotehnologije u budućnosti. Pod njegovim rukovođenjem i sa nekoliko entuzijastičnih ljudi Institut BioSens je prerastao u respektabilnu instituciju sa gotovo 200 zaposlenih i više od 70 doktora nauka. BioSens je prepoznat kao veoma multidisciplinarna  institucija koja primenjuje najsavremenije tehnologije kao što je veštačka inteligencija, robotika, nanotehnologije i novi materijali budućnosti u biosistemima – poljoprivredi, životnoj sredini i medicini. Pored izvrsne nauke institut BioSens je veoma posvećen saradnji sa industrijom kroz stvaranje inovativnih rešenja i snažno podržava razvoj inovacija kroz akceleratorske programe i formiranje spin-of kompanija. BioSens je dao značajan doprinos digitalnoj transformaciji javnog sektora i društva u Srbiji kroz razvoj različitih sistema, primarno u sektoru poljoprivrede.

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Profesor je računarskih nauka Prirodno-matematičkom fakultetu Univerziteta u Novom Sadu. Na istom univerzitetu je studirao elektrotehniku i odbranio doktorat 2004. godine. Od tada je posvećen istraživanjima i razvoju u oblasti mašinskog učenja – danas popularne veštačke inteligencije. Jedan je od pionira u ovoj oblasti u Srbiji što je rezultiralo većim brojem odbranjenih doktorata i razvojem brojnih inovativnih rešenja za industriju. Obavljao je funkciju direktora departmana za energetiku, elektroniku i telekomunikacije Fakulteta tehničkih nauka Univerziteta u Novom Sadu i člana matičnog odbora za elektrotehniku, telekomunikacije i informacione tehnologije Ministarstva prosvete i nauke Republike Srbije. Član je nacionalnog Saveta za veštačku inteligenciju i Komiteta za veštačku inteligenciju Saveta Evrope.

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Prof. dr Crnojević je član 10-Member-Group, prestižne grupe eksperata koji imaju ulogu konsultanata Generalnog sekretara Ujedinjenih Nacija u oblasti uloge nauke, tehnologije i inovacija u realizaciji ciljeva održivog razvoja UN SDG koje su usvojile Ujedinjene Nacije. Objavio je više od 100 radova u renomiranim časopisima i na konferencijama i bio recenzent za neke od najeminentnijih časopisa u njegovoj oblasti. Njegova sadašnja interesovanja vezana su za ulogu veštačke inteligencije u ostvarivanju ciljeva održivog razvoja UN SDG i budućnosti čovečanstva.

Dr Sanja Brdar je naučni saradnik i rukovodilac Centra za informacione tehnologije na Institutu BioSens. Doktorirala je na Univerzitetu u Novom Sadu 2016. Pre početka doktorata provela je dve godine u softverskoj kompaniji u timu za razvoj baza podataka. Desetomesečnu Basileus stipendiju za istražvački boravak u Laboratoriji za bioinformatiku na Univerzitetu u Ljubljani dobila je 2010. godine i time postavila osnove za doktorat u oblasti fuzije podataka/znanja u bioinformatici. Aktivno je uključena u mnoge projekte, uključujući H2020 Antares, Dragon, Bestmap i Flekigrobots i predavač je na dva master predmeta na Prirodno-matematičkom fakultetu, smer Nauka o podacima. Njena istraživačka interesovanja uključuju mašinsko učenje, objašnjivu veštačku inteligenciju i bioinformatiku. Radi na ansambalskim metodama, fuziji podataka, klasterizaciji i prediktivnom modelovanju sa primenom u biologiji, poljoprivredi, životnoj sredini i javnom zdravlju. Učesnica je na svetskim izazovima iz nauke o podacima i sa timom istraživača postigla je značajne rezultate: treće mesto na Nokia Mobile Data Challenge (2012), finalista Orange France, Data for Development Challenge (2013), finalista / pobednik / treće mesto na Singenta Crop Challenge (2016, 2017, 2019) i finalista UNDP izazova depopulacije Srbije (2020).

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Dr Oskar Marko je rukovodilac Centra za informacione tehnologije. Njegovo istraživanje usmereno je ka primeni naprednih metoda mašinskog učenja i evolutivnih algoritama u poljoprivredi. Treću godinu osnovnih studija je pohađao je na “Siti univerzitetu” u Londonu, gde je odbranio diplomski rad u domenu obrade signala. Vodio je tim BioSensa koji je razvio nove algoritme iz oblasti obrade velikih podataka koje su za cilj imale predikciju prinosa, distribuciju semena i optimizaciju strategije setve, koje su Institutu osigurale prvo mesto na Sindžentinom “Izazovu za useve” i CGIAR-ovom “Inspirišućem izazovu”. Aktivno je uključen u mnoge projekte iz poziva Horizont 2020, među kojima su Antares, Cybele, Dragon i Flexirobots, kao i u projekte sa kompanijama poput Krivaje, MK Agri, Delte i drugih poljoprivrednih kompanija i osiguravajućih kuća.

Nikola Obrenović radi kao stariji istraživač pri Centru za informacione Tehnologije, Instituta BioSens, Univerziteta u Novom Sadu. Diplomu doktora nauka iz oblasti elektrotehničkog i računarskog inženjerstva, Nikola je stekao u oktobru 2015. godine na Fakultetu tehničkih nauka u Novom Sadu. Tokom doktorskih studija, Nikola se bavio oblasti razvoja baza podataka vođenim modelima, brzom izradom softverskih prototipova i automatskom verifikacijom dizajna softvera. Između jula 2017. i juna 2019. Godine, Nikola je radio kao postdoktorski saradnik u nauci i nastavi na Federalnoj politehničkoj školi u Lozani (École polytechnique fédérale de Lausanne, EPFL), Švajcarska. Tamo, Nikola je učestvovao u razvoju i implementaciji nekoliko evropskih i švajcarskih nacionalnih projekata iz oblasti matematičke optimizacije i operacionih istraživanja. Pre nego što se pridružio Institutu BioSens u julu 2019, Nikola je bio docent na Departmanu za energetiku, elektroniku i telekomunikacije, Fakulteta tehničkih nauka u Novom Sadu. Oblasti njegovog istraživanja su matematička optimizacija, operaciona istraživanja, mašinsko učenje, baze podataka i softversko inženjerstvo vođeno modelima.
Uz to, Nikola Obrenović ima veliko praktično iskustvo iz softverske industrije. Tokom svoje karijere, Nikola je radio kao softverski inženjer i arhitekta u nekoliko internacionalnih softverskih kompanija i nezavisnih projekta. Kao najbitnije, izdvaja se Nikolin angažman kao softverski arhitekta na projektu “JourneyTree – Advanced Customer Analytics Made Simple”, finansiranom od strane DataPitch programa, pod Horizont 2020 istraživačkim i inovacionim programom Evropske Komisije.

Miloš Pandžić je istraživač pripravnik čije interesovanje pokriva nekoliko istaživačkih oblasti: osmatranje Zemlje, daljinsku detekciju, obradu slike i analitiku podataka. Trenutno je student doktorskih studija odseka za Geodeziju i geoinformatiku na Građevinskom fakultetu, Univerziteta u Beogradu, gde je takođe završio i osnovne i master akademske studije. U 2015. godini dobio je stipendiju vlade Austrije (OeAD) i proveo 4 meseca na Tehničkom univerzitetu u Beču gde je analizirao geometrijski kvalitet Sentinel-2 satelitskih multispektralnih snimaka. Iz ovog istraživanja je proizašao i Milošev master rad. Nakon završetka studija, radio je u Finskom geoprostornom istraživačkom institutu (Finnish Geospatial Research Institute - FGI) na više projekata finansiranih od strane Finske Akademije i Evropske svemirske agencije. Tu je radio na segmentaciji i detekciji promena na satelitskim slikama za potrebe poljoprivrede, urbanizma i šumarstva. U 2018. godini pridružio se Institutu BioSens gde radi na fuziji podataka optičkih i radarskih slika za procenu prinosa, klasifikaciju useva, praćenje razvoja useva, itd. Učestvuje u realizaciji nekoliko nacionalnih i internacionalnih projekata uključujući Horizon 2020 projekte Antares, Cybele, Dragon, PARSEC i agROBOfood.

Dr Marko Panić je naučni saradnik u oblasti tehnološko-tehničkih nauka – informacionih tehnologija na institutu BioSens. Tema doktorata mu je vezana za razvoj algoritama za ubrzanu rekonstrukciju slika iz nepotunih merenja korišćenjem retke reprezentacije signala I statističkih Markovljevih slučajnih modela Njegov pravac istraživanja obuhvata statističko modelovanje multidimenzionalnih podataka dobijenih sa različitih senzora. Trenutno radi na analizi I obradi slika dobijenih sa hiperspektralnih I multispektralnih kamera korišćenjenem naprednih metoda mašinskog učenja sa primenom u proceni kvaliteta voća I povrća. Kao član tima BioSens ucestvovao je na Sindžentinom takmičenju “Izazovu za useve” koje rezltiralo osvajanjem prvog mesta. Aktivno je uključen u mnoge projekte iz poziva Horizont 2020, među kojima su Antares, Cybele, Dragon i Flexirobots, kao i u projekte sa kompanijama poput Krivaje, MK Agri i Delta Agrar.

Gordan Mimić je viši naučni saradnik na Biosens Institutu. Gordan Mimić je završio osnovne (2011), master (2012) i doktorske (2016) studije fizike-meteorologije na Prirodno-matematičkom fakultetu, Univerziteta u Novom Sadu. Radio je u Republičkom hidrometeorološkom zavodu Srbije tokom perioda 2011-2012, u odeljenju za numeričku prognozu vremena. Gordan Mimić je radio kao asistent na raznim predmetima iz oblasti meteorologije na Univerzitetu u Novom Sadu (Prirodno-matematički fakultet i Poljoprivredni fakultet) u periodu 2012-2017 godina. Trenutno se bavi prediktivnom analitikom u oblasti agrometeorologije. Predvodio je tim BioSensa koji je razvio novi algoritam za klasifikaciju hibrida kukuruza koji su otporni na sušu i toplotni stress, i za koji su osvojili treću nagradu na Sindžentinom “Izazovu za useve” 2019. godine. Učestvovao je na nekoliko nacionalnih i međunarodnih projekata. Gordan Mimić je koautor 33 naučna rada, 26 konferencijskih radova, 3 tehnička rešenja i 1 poglavlja u knjizi.

Branislav Pejak je student doktorskih studija Elektrotehničkog i kompjuterskog inženjerstva na Fakultetu tehničkih nauka, Univerziteta Novi Sad, na kojem je takođe završio osnovne (2017) i master (2018) studije u oblsti Biomedicinskog inženjerstva. Tokom osnovnih i master studija bio je stipendista Ministarstva obrazovanja, nauke i tehnološkog razvoja, Republike Srbije. Od marta 2018. godine zaposlen je u Centru za informacione tehnologije, Instituta BioSens, na poziciji mlađeg istraživača. Tema njegovog istraživanja je analiza podataka sa satelitskih snimaka. Trenutno se bavi razvojem GAN algoritama mašinskog učenja za klasifikaciju i predikciju prinosa na bazi multispektralnih satelitskih snimaka. Aktivno je uključen u mnoge projekte sa poziva Horizont 2020, među kojima su Antares, Cybele, Dragon i AgRoboFood koji imaju za cilj upotrebu informaciono-komunikacionih tehnologija u poljoprivredi poput kreiranja mapa za selektivno uzorkovanje i đubrenje zemljišta, detekciju navodnjavanih površina, procenu prinosa i proteinskog sastava useva.

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Andrea Simeon je završila osnovne i master studije u oblasti Primenjene matematike na Prirodno-matematičkom fakultetu u Novom Sadu, na Departmanu za matematiku i informatiku, redom 2018. i 2020. godine. Njene osnovne studije su bile pretežno orijentisane ka finasijama, ali master studije ne nastavlja u tom pravcu nego upisuje program na engleskom jeziku, Nauka o podacima. Tokom studija je zajedno sa kolegom učestvovala na takmičenju organizovanom od strane Filip Moris kompanije, “Metagenomics diagnosis for inflammatory bowel disease challenge” na kojem su osvojili 7. mesto. Ovo takmičenje je poslužilo kao inspiracija za dalji rad na temu mikrobiom podataka. S tim ciljem, 2020. godine upisuje doktorske studije Infromatike na Prirodno-matematičkom fakultetu u Novom Sadu. Trenutno je zaposlena u BioSense Institutu kao istraživač-priprevnik sa temom za istraživanje u oblasti analize mikrobiom podataka i primene statističkih tehnika kao i mašinskog učenja u mikrobiom studijama.

Tijana Nikolić je naučni sardnik na BioSense Institutu, doktorirala je na Prirodno-matematičkom fakultetu Univerziteta u Novom Sadu, oblast ekologija. Radi na očuvanju prirodnog i poluprirodnog staništa u poljoprivrednim predelima, potrebnim za zaštitu agro biodiverziteta, poboljšanje kvaliteta zemljišta i bezbednu proizvodnju hrane. Glavni fokus je na integrisanom upravljanju predelom koji podrazumeva proizvodnju hrane, ošuvanje biodiverziteta i prilagođavanje na klimatske promene, različitih sektora i zainteresovanih strana kako bi se zadovoljio niz kratkoročnih i dugoročnih društvenih potreba.

Dr Branko Šikoparijaje naučni savetnik u Centru za biosisteme i rukovodilac kvaliteta (ISO17025) u Laboratoriji za palinologiju na Prirodno-matematičkom fakultetu Univerziteta u Novom Sadu. Fokus istraživanja je aerobiolgija, a trenutno je zainteresovan za razvoj detekcije i identifikacije bioaerosola u realnom vremenu. Branko Šikoparija je publikovao i recenzirao naučne radove u brojnim naučnim časopisima. Radi i kao pomoćnik urednika časopisa Aerobiologia. Koordinator je RealForAll mreže za autmatski monitoring bioaerosola u Panonskoj niziji. Takođe, aktivan je član EUMETNET AutoPollen programa, zamenik predsedavajućeg COST Action CA18226, i predsedavajući radne grupe Evropskog Aerobiološkog Udruženja za kontrolu kvaliteta. Vodeći je istraživač BREATHE projekta koji u okviru PROMIS programa finansira Fond za nauku Republike Srbije.

Ovaj projekat je finansiran od strane Evropske Unije, iz programa za Istraživanje i Inovacije-Horizont 2020, pod sporazumom o grantu broj 739570.

         

Horizont 2020

Ubrzanim rastom ljudske populacije na globalnom nivou, prehrambeni sistemi suočavaju se sa sve većim pritiscima. Takođe, ishrana kao faktor rizika za nastanak hroničnih nezaraznih bolesti, kao i rastuća potražnja potrošača za zdravim proizvodima, dodatno podstiču razvoj inovacije, razvoj novih proizvoda i reformulacije u oblasti hrane.

Mi razvijamo, optimizujemo i primenjujemo nove tehnologije, poput 3D štampanja hrane, kao kontrolisane platforme za proučavanje odnosa između sastojaka hrane, njihove strukture i interakcija između hrane i mikroorganizama grastrointestinalnog trakta, uz razvoj nutritivno i senzorno prilagođenih prototipova hrane za zdravu i održivu ishranu. Korišćenjem održivih funkcionalnih sastojaka i alternativnih proteina, dobijenih iz insekata i drugih nedovoljno iskorišćenih bioresursa, istražujemo njihov bioaktivni potencijal u cilju identifikacije jedinjenja sa mogućim pozitivnim efektima na zdravlje.

Centar za biosisteme, u bliskoj saradnji sa Centrom za senzorske tehnologije i Centrom za informacione tehnologije, integriše dizajn formulacija hrane zasnovan na podacima i optimizaciju procesa sa fiziološki relevantnim mikrofluidnim in vitro validacionim sistemima, čime zajedno doprinosimo razvoju funkcionalne hrane zasnovane na naučnim dokazima i inovacijama.

Inovacije u hrani i zdravlju

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BioSens akcelerator je intenzivan, tromesečni program namenjen AgTech startapima u ranoj fazi i predstavlja snažan most između nauke i biznisa. Program omogučava startapima da rastu, ubrzaju svoje putovanje od početne AgTech ideje do biznisa, i reše ključne izazove u poljoprivredno-prehrambenom sektoru. BioSens akcelerator je jedini takve vrste u Srbiji koji pruža podršku domaćim i regionalnim IT preduzetnicima usredsređenim na stvaranje inovativnih i revolucionarnih digitalnih rešenja za poljoprivredu.

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Prvi BioSens akcelerator počeo je u oktobru 2019. godine i tokom tri meseca koja su bila ispunjena interaktivnim radionicama i specijalnim treninzima, 8 timova savladalo je nekoliko veština: kako razviti poslovnu ideju, definisati poslovni model, identifikovati potrebe budućih korisnika i sprovesti detaljnu analizu tržišta i konkurencije. Zahvaljujući vrhunskoj tehničkoj i poslovnoj podršci, kao i smernicama prominentnih stručnjaka- mentora, startapi su svoju početnu ideju podgili na viši nivo. Nakon veoma uspešnog prvog BioSens akceleratora, Institut BioSens nastavlja da podržava preduzetnike. U realizaciji BioSens akceleratora 2020., Institut je udružio snage sa Evropskim fondom za Jugoistočnu Evropu i najboljim B2B akceleratorom u Evropi, Startup Wise Guys.

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Pored tehničke, poslovne i mentorske podrške, BioSens akcelerator omogućio je timovima i da se upoznaju sa strategijom prikupljanje sredstava, a startapi koji su učestvovali u programu prikupili su 140 hiljada evra tokom šest meseci. Ideja BioSens akceleratora jeste da podstakne preduzetnički mentalitet u našoj zemlji, a time i ekonomski razvoj, kao i da motiviše regionalne IT preduzetnike da hrabro stvaraju svoje proizvode.

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Saznajte više: <a href="http://accelerator.biosense.rs/">BioSens akcelerator</a>

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BioSens akcelerator

Za analizu problema i prikupljanje podataka, BioSens koristi različite uredjaje za akviziciju slike. Razlike između uređaja tiču se širine spektra elektromagnetnog zračenja i broja spektralnih pojasa u kojima rade. Hiperspektralne kamere obuhvataju vidljivi i blisko-infracrveni deo spektra (VNIR) kao i isključivo blisko-infracrveni deo spektra (NIR) i koriste se za laboratorijske analize, dok se termalna i multispektralna kamera uglavnom koriste za terenski rad.

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Laboratorijske analize su preciznije jer hiperspektralne kamere mogu imati rezoluciju i od preko 200 kanala, a nakon kalibracije korišćenjem referentih ploča se dodatno povećava pouzdanost merenja. Ipak, kako jedna hiperspektralna slika može imati i do 2GB, ovakve slike zahtevaju velik memorijski prostor i veoma efikasne algoritme obrade slike. Za redukciju količine podataka biraju se najinformativniji spektralni kanali sa visokom odnosom snage signala i šuma ili se primenjuju standardni postupci analize glavnih komponenata (PCA ili ICA). Slike koji stižu sa termalnih i multispektralnih kamera takođe zahtevaju predobradu ali su ovi postupci više fokusirani na smanjenje šuma filtriranjem slika. Sledeći korak, zavisno od problema koji se razmatra, je analiza slike koja obuhvata tehnike izdvajanja obeležja, grupisanja podataka po sličnosti i primene dubokih neuralnih mreža radi segmentovanja slike na regione od interesa. Dalje se koriste dodatni algoritmi za finalnu prezentaciju rezultata i dobijeni rezultati se prosleđuju algoritmima mašinskog učenja za dalju analizu podataka.

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U praksi se ove napredne kamere koriste za razvoj algoritama za detekciju infekcija na povrću ili otkrivanja modrica na voću i to nekoliko dana pre nego što simptomi postanu vidljivi golim okom. Značajan je i njihov doprinos u praćenju uslova ekosistema i predikciji prinosa, a razvijeni algoritmi pružaju dodatnu informaciju o stanju biljaka, daju procenu roka trajanja poljoprivrednih proizvoda kao, predviđaju prinos i daju objektivne predloge za donošenje odluka nakon žetve.

Hiperspektralne, multispektralne i termalne slike

Upotreba robotike u poljoprivredi je oblast u nauci koja se sve više razvija i sa novim rešenjima dospeva na tržište. Značajan napredak u ovoj oblasti uzrokovan je nedostatkom radne snage, porastom troškova proizvodnje kao i zamorom i repetitivnošću koje određeni zadaci nose sa sobom. BioSens koristi robote kao platforme koji se kreću i prikupljaju informacije o usevu. Ove platforme su opremljene senzorima koji su komercijalno dostupni i senzorima koje je razvila grupa za dizajn senzora Instituta BioSens.

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Najveći projekat na kom je institut radio je vezan za analize tla na terenu. Predloženi sistem se kreće kroz polje i vrši uzorkovanje zemljišta i analizu parametara (makronutrijenti, mikronutrijenti, pH, elektroprovodljivost) kao i georeferenciranje rezultata analize. Kompletan proces je autonoman jer je moguće unapred definisati rutu koju će robotska platforma preći na parceli koristeći GPS koordinate i 3D Real-Time-LIDAR sistem za rekonstrukciju okruženja i izbegavanje mogućih prepreka. Kao rezultat analize, generišu se višeslojne mape gde svaki sloj predstavlja jedan od parametara dobijenih predloženim sistemom. Poljoprivrednik će pomoću ovih mapa moći da donese pravu odluku, poboljša proizvodnju i u potpunosti iskoristi preciznu poljoprivredu u pogledu efikasnosti, povećanja prinosa i održivosti poljoprivredne proizvodnje.

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Robotika

Na Institutu BioSens koristimo slike kvadkoptera DJI Inspire 1 kako bismo dobili bolji uvid u stanje useva i preciznije planirali i upravljali agroekološkim aktivnostima. Ova tehnologija omogućava prikupljanje podataka visoke rezolucije ispod nivoa oblaka sa mnogo više detalja nego što se može videti u satelitskim snimcima. Skup podataka uključuje slike iz vidljivog dela spektra kao i slike dobijene modifikovanom NDVI kamerom. Takođe koristimo i Flir termalnu kameru za merenje temperature na velikim površinama u hetoregenim staništima. Bespilotne letelice su jednostavne za upotrebu, jer se većina mapiranja terena i prikupljanja podataka sada sprovodi autonomno, prema zadatim granicama terena uz pomoć aplikacija za planiranje leta.

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Za preklapanje slika koristimo softvere Pix4D i AgisoftPhotoscan kako bismo generisali ortomozaike, mape vegetacionih indeksa i digitalne modele površine. Ove orto-mape su važni slojevi informacija i mogu se integrisati u GIS sisteme zajedno sa slojevima podataka dobijenih iz drugih izvora. Ovakvi skupovi podataka se zatim obrađuju i koriste za obuku naprednih algoritama mašinskog učenja.

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BioSens koristi bespilotne vazdušne sisteme za otkrivanje nedostatka hranljivih materija u biljkama, napada štetočina, prekomerne suše ili vlage kao i drugih faktora koji utiču na razvoj useva, kao i za povećanje efikasnosti pri donošenju odluka. Posebno su korisni za površine od nekoliko desetina hektara, poput voćnjaka i vinograda, jer skeniranje velikih ratarskih parcela zahteva mnogo vremena. Bespilotne letelice su se pokazale i kao efikasni alati za praćenje stanja ekosistema. Pokrivajući veća prostranstva, omogućavaju pristup informacijama o tipu staništa u kojem rastu i razvijaju se živi organizmi od insekata do sisara. Njihova upotreba dovela je do revolucionarnih otkrića na poljima prostorne ekologije i poljoprivrede, i omogućila nam dobijanje podataka visoke rezolucije za donošenje optimalnih odluka sa visokom preciznošću.

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NDVI map of sugar beet crop

Obrada slika sa dronova

BioSens je dom habu za Sentinel podatke i kao takav je korisnik proizvoda Kopernikus programa Evropske svemirske agencije (ESA). Pre lansiranja Sentinel satelita, naša istraživanja su se zasnivala na snimcima sa satelita Landsat 7 i 8, kao i RapidView-a, ali su danas primarno usmerena na podatke koji potiču sa Sentinel 1 i 2 misija. Slike sa Sentinela 2 sadrže 13 spektralnih kanala i dostupne su na svakih 5 dana u zavisnosti od oblačnosti, dok Sentinel 1 koji snima radarske slike omogućava praćenje čak i u slučaju oblačnosti. Na ovaj način omogućeno je kontinualno osmatranje površine Zemlje u visokoj rezoluciji uključujući praćenje poljoprivrednje proizvodnje kroz sve faze rasta biljaka.

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Od 2012. godine proizvodimo mape upotrebe zemljišta za Autonomnu Pokrajinu Vojvodinu, gde klasifikujemo zemljište u šume, vodene mase, urbane površine i različite tipove useva. Ovakve mape su nam omogućile da mapiramo životinjska staništa, posmatramo kako se menjaju tokom godina i ispratimo plodored na proizvodnim parcelama. Oslanjajući se na principe fuzije podataka, uspeli smo da dođemo do sub-pikselske rezolucije korišćenjem višestrukih izvora podataka sa heterogenim mrežama piksela. Druga grana našeg istraživanja se bavi prognozom prinosa useva. Na osnovu podataka sa terena dobijenih pomoću digitalnih knjiga polja, kraudsorsinga ili mapa prinosa, obučavamo napredne modele mašinskog učenja u cilju što bolje procene prinosa. Ovakvi modeli koriste više od 30 satelitskih vegetacionih indeksa koji uključuju domensko znanje o usevima, a njihova tačnost se povećava kako napredujemo kroz faze razvoja biljaka. Vegetacioni indeksi su takođe odlični pokazatelji procene štete jer neki od njih veoma dobro reaguju na pojavu prirodnih nepogoda poput suše, požara i izmrzavanja. Na ovaj način se osiguravajuća društva uključuju u partnerstvo sa Institutom, zajedno sa državnim oragnima, istraživačkim institucijama i poljoprivrednim proizvođačima koji naše rezultate koriste za optimalno donošenje odluka o navodnjavanu i distribuciji đubriva i pesticida.

Obrada satelitskih slika

Evropska svemirska agencija je kroz Kopernikus program lansirala plejadu Sentinel satelita. Sa svojih 7 milijardi evra ulaganja, Kopernikus je najambiciozniji poduhvat satelitskog snimanja Zemlje u istoriji svemirskih istraživanja, a podaci sa satelita su našli široku primenu u praćenju klimatskih promena, građevinarstvu, saobraćaju i reagovanju u kriznim situacijama poput zemljotresa i vremenskih nepogoda. Podaci generisani kroz ovaj projekat su besplatni i potpuno otvoreni za svakoga. Veruje se da će evropska privreda do 2030. generisati čak 30 milijardi evra kroz preduzeća čija delatnost se zasniva na obradi satelitskih slika. Zbog ovako velikog zamajca koji Kopernikus daje visokotehnološkim startapovima i velikim kompanijama, često se koristi izraz „Kopernikus ekonomija“.

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Godine 2017. Srbija je postala jedina zemlja koja kao pridruženi član EU ima širokopojasni pristup ESA-inim slikama. Hab za Sentinel podake koji je otvoren na Institutu BioSens dobija podatke sa satelita, a nakon toga se podaci obrađuju na serverima visokih performansi. Za samu diseminaciju satelitskih slika zadužen je softver zasnovan na Javi - DHuS (Dejta Hab Softver). DHuS je softver otvorenog koda koji obezbeđuje jednostavno veb okruženje za interaktivno otkrivanje i preuzimanje podataka, kao i odličan interfejs za programiranje aplikacija (API) koji korisnicima omogućava pristup podacima preko sopstvenih računarskih programa, skripti ili klijentskih aplikacija. Time se automatizuje preuzimanje u okviru njihovog radnog procesa, a sami snimci postaju dostupni zainteresovanim stranama iz svih krajeva sveta.

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ESA-ine satelitske slike se već uveliko koriste na Institutu, za dobijanje mapa setvene strukture, procenu prinosa i praćenje rasta i razvoja useva u čiju svrhu se koriste brojni vegetacioni indeksi od kojih su najpoznatiji „normalizovani diferencijalni vegetacioni indeks“ (NDVI), „unapređeni vegetacioni indeks“ (EVI), „indeks vegetacije prilagođen zemljištu“ (SAVI) i mnogi drugi. Očekujemo i da će srpska preduzeća iskoristiti ovu priliku, razviti industrijska rešenja na bazi Sentinel slika i sa svojim inovacijama izađi na svetsko tržište.

Hab za Sentinel podatke

Analitika velikih podataka se zasniva na obradi heterogenih izvora podataka, različitih rezolucija, koji se prikupljaju u nejednakim vremenskim intervalima. Osim satelitskih podataka, Grupa za veštačku inteligenciju Instituta BioSens prikuplja i koristi podatke dobijene pomoću elektromagnetne sonde koja meri električnu provodnost zemljišta, kao i brojne druge instrumente koji se montiraju na poljoprivredne mašine ili koriste iz ruke. Električna provodnost zemljišta je odličan indikator teksture zemljišta, njene kompaktnosti i hemijskog sastava. Ovi parametri mogu se odrediti na različitim dubinama u zavisnosti od potreba i interesovanja korisnika prema tipu biljaka. Mape elevacije terena nisu ništa manje važne s obziorm na to da određuju pravac i smer oticanja nutrijenata koji je u tesnoj sprezi sa prinosom, dok se ručni uređaji, poput Plant-O-Meter-a razvijenog od strane istraživača Instituta BioSens, uspešno koriste pri monitoringu rasta useva.

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Korišćenjem tehnika fuzije podataka, heterogeni podaci se agregiraju u jedinstven skup koji se koristi za iscrtavanje, odnosno određivanje menadžment zona. Određivanje zona vrši se algoritmima klasterizacije bilo u normalnoj ili težinskoj formi, što zavisi od relativne važnosti ulaznih podataka. Ove menadžment zone međusobno imaju različite osobine, ali se u okviru jedne zone zemljište može posmatrati homogenim. To dalje omogućava davanje preporuka o poljoprivrednoj proizvodnji za svaku zonu ponaosob, u skladu sa njenim specifičnostima. Ovakav pristup donošenju odluka u okviru jedne parcele osnova je za preciznu poljoprivredu, a ujedno je i prvi korak pri određivanju optimalne lokacije za postavljanje senzora jer se na taj način maksimizuje njihova efikasnost.

Analitika prostornih podataka

Fokus našeg interesovanja je unapređenje poznavanja ekoloških interakcija, opstanak divljih životinja i očuvanje njihovih staništa. Naša istraživanja se sprovode na terenu kako bi se sagledale i razumele posledice promena životne sredine, u prostoru i vremenu, u predelima koji se intezivno koriste od strane ljudi. Odgovore na pitanja u vezi sa interakcijom odabrane vrste i procesa koji se posmatra pratimo na individualnom nivou, (meta)populacionom nivou i na nivou zajednica. Glavni cilj je da se unapredi trenutna i omogući efikasna buduća zaštita prirode.

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Spermophilus citellus (Foto - Tijana Nikolić)

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Coracias garrulus (Foto Dmitrije Radišić)

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Canis lupus (Foto -Zavod za zaštitu prirode Srbije)

Konzervacija: Ugrožene vrste životinja

Sa ciljem pravovremenog registrovanja promena u ekosistemima, deo aktivnosti usmeravamo prema grupi insekata oprašivača, osolikim muvama, koje su se pokazale kao veoma dobri bioindikatori. Njihovo prisustvo i brojnost može ukazati na nivo narušenosti ekosistema. Tokom terenskih ekspedicija dobijamo informacije o diverzitetu i distribuciji vrsta. Korišćenje modernih tehnologija na terenu, kao što su dronovi opremljeni različitim kamerama, donosi informacije o različitim parametrima vezanim za kvalitet i promene u staništima.

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Kombinovanjem podataka o distribuciji vrsta i onih o promenama u staništu, korišćenjem različitih modela distribucije, analizama fukcionalne kompozicije zajednica i prostornim analizama predviđamo efekte globalnog zagrevanja na distribuciju vrsta i određujemo nivoe antropogenog pritiska na funkcionalnost ekosistema.

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Na osnovu rezultata naših istraživanja predlažemo setove upravljačkih mera koji mogu dati pozitivne efekte ne samo na očuvanje ekosistema, nego i na porast količine useva kroz podizanje brojnosti oprašivača u različitim poljoprivrednim kulturama.

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Bioindikatori i promene u životnoj sredini

Sa pojavom neparametaskih modela mašinskog učenja i rastućom popularnošću dubokih neuronskih mreža, većina klasifikatora i regresora koji se danas koriste su “crne kutije”. Tačnost ovih modela često je neuporedivo bolja nego kod konvencionalih modela, a iako znamo matematiku koja se krije iza njih, ipak ne možemo direktno da zaključimo kompleksnu vezu izmeću ulaznih i izlaznih podataka. Velika tačnost modela je od izuzetnog praktičkog značaja, ali kako bismo imali bolje razumevanje problema i kako bismo pomerili granice nauke moramo da zagrebemo dublje u način njihovog funkcionisanja.

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Oslanjamo se na nekoliko tehnika otkrivanja znanja, koje se baziraju na indirektnim posmatranjima modela mašinskog učenja. Jedan od njih je test slučajne permutacije. Pomoću njega procenjujemo važnost obeležja na osnovu smanjenja tačnosti modela nakon slučajne permutacije obeležja u skupu podataka. Morisonova i Soboljeova analiza neizvesnosti i osetljivosti nam sa druge strane govore o mogućim izlazima sistema i interakcijama između ulaznih parametara. Analiza objašnjivosti modela ide korak dalje. Za svako obeležje koje se koristi u prediktivnom modelu, kroz aproksimaciju zasnovanu na uzorkovanju , možemo da procenimo pozitivan ili negativan uticaj na izlaznu promenljivu, što nam omogućava da domensko istraživanje usmerimo tamo gde je najefikasnije. Ovi i drugi modeli nam omogućavaju da odredimo uticaj različitih parametara zemljišta i vremena na prinos useva i da utvrdimo koji hibridi kukuruza su otporni na sušu, a koje sorte soje imaju stabilan prinos.

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Otkrivanje znanja

Značajan deo BioSensovih istraživanja u oblasti Velikih Podataka je fokusiran na dva tipa podataka koji zahtevaju velik memorijski kapacitet za skladištenje i ekstremno efikasne algoritme za obradu podataka koji mogu da se izvrše u zahtevanom vremenskom roku.

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Prvi od njih su satelitske slike. Sentinel 2 sateliti isporučuju nove slike u 13 pojasa za bilo koju tačku na Zemlji, približno svakih 5 dana. Ove slike dolaze u isečcima dimenzija 100x100 km2, sa prostornom rezolucijom od 10m. U okviru Cybele Projekta, na primer, analiziramo satelitske slike cele Evrope da bismo klasifikovali polja soje, što znači da treba da prođemo kroz stotine isečaka i primenimo nad njima algoritam za klasifikaciju. U takvim situacijama, radimo paralelizaciju koda radi bolje i efikasnije eksploatacije multicore server arhitekture, ubrzavajući procese korišćenjem biblioteke Cython, dok u isto vreme prilagođavamo implementaciju da umanjimo računarsku kompleksnost algoritama.

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Drugi tip podataka su liste poziva korisnika mobilne telefonije i oni zadovoljavaju sve “V”-ove analitike velikih podataka. Ovi podaci se sastoje iz liste uspostavljenih poziva, razmenjenih poruka i registrovanog internet saobraćaja i ogromne su veličine. Važno pitanje u obradi je vezano za privatnost i izdvajanje korisnih informacija iz anonimizovanih i agregiranih skupova podataka predstavlja veliki izazov. Takođe, iako su podaci semi-struktuirani, tradicionalna RDBMS tehnologija za analitiku podataka nije primenljiva na skupove ove veličine. U tu svrhu razvijamo tokove rada za obradu korišćenjem Apache porodice tehnologija - Spark, Hadoop i Hive. One nam daju mogućnost da dobijemo dodatni uvid u ljudsko ponašanje, šablone kretanja i saznamo više o interakciji između ruralnih i urbanih oblasti.

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Analitika Velikih Podataka

U biološkim naukama se trenutno generišu velike količine podataka, posebno na molekularnom nivou. Tokom poslednje decenije, stopa generisanja ovih podataka premašila je Murov zakon o dupliranju količine na svakih 18 meseci, sa nedavno procenjenim udvostručenjem na svakih 7 meseci. Raznovrsni podaci molekularno biološkog porekla se često nazivaju “omika” (engl. omics) da bi se kolektivno okarakterisali različiti izvori - genomika, transkriptomika, proteomika, metabolomika, itd. Raznovrsnost i kvantitet ovih podataka su izazvali fundamentalni pomak u istraživanjima molekularne biologije. Bioinformatika je nastala iz ambicije da se razviju moćniji alati za analizu i izvođenje zaključaka iz “omičkih” podataka.

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Bioinformatički tim Instituta BioSense razvija alate i metode za različite oblasti u okviru molekularne biologije, kao što su funkcionalna genomika, metagenomika, mapiranje lokusa kvantitativnih osobina (engl. quantitative trait loci mapping) i izučavanje genomskih asocijacija (engl. genome-wide association studies) za fenotipske osobine od značaja, mapiranje abiotičke i biotičke otpornosti na stres, itd. Da bismo odgovorili na računarske i analitičke izazove oslanjamo se na metode mašinskog učenja, algoritme teorije grafova i teorije informacija, kompresiju podataka i integraciju podataka/domenskog znanja. Većina podataka kojima se bavimo spadaju u domen poljoprivrede, najčešće vezane za genomiku useva, interakciju genoma i fenoma, interakcije genotipa sa uslovima sredine ali i podatke o mikrobiomu vezane za ljudsko zdravlje i zdravlje useva.

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Bioinformatički algoritmi takođe imaju važnu ulogu i u razvoju tehnologije čuvanja podataka u formi DNK. Velike količine generisanih podataka zahtevaju efikasna rešenja za njihovo skladištenje. Dokazano je da skladištenje podataka zasnovano na DNK ima veliki potencijal, naročito zbog svoje dugovečnosti i gustine zapisa informacija, te postaje atraktivna alternativa konvencionalnim sistemima za skladištenje podataka. U ovoj oblasti fokusirani smo na analizu fundamentalnih performansi ovakvih sistema, osmišljavanje novih arhitektura za skladištenje i razvoj odgovarajućih algoritama. Neki od problema na kojima trenutno radimo su dizajniranje sekvenci, mehanizmi za ispravljanje grešaka i algoritmi za kodiranje i dekodiranje.

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Bioinformatika

Duboko učenje je novi metod mašinskog učenja. Zbog svoje specifičnosti i odličnih performansi u obradi slike često se posmatra kao zasebna oblast u okviru veštačke inteligencije. Najbolje radi na velikim skupovima podataka koji imaju multi-dimenzionalne karakteristike, sa kakvim se često susrećemo na BioSensu.

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U prvu grupu podataka koje posedujemo spadaju hiperspektralne, multispektralne i termalne slike, napravljene u laboratoriji ili na terenu. Cilj njihove obrade je segmentacija slike i prepoznavanje karakterističnih obrazaca, kao što su počeci truljenja i fizička oštećenja na jabukama, infekcija peteljke paradajza ili detektovanje klasova pšenice. Brojne arhitekture su testirane sa različitim parametrima, poput broja konvolucionih i potpuno povezanih slojeva, vrsta aktivacionih funkcija i slučajne eliminacije dela mreže da bi se izbeglo preučenje. Svaka arhitektura je optimizovana za specifičan problem, uzimajući u obzir veličinu slika i broj uzoraka u bazi podataka.

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Druga grupa skupa podataka se sastoji od merenja čestica u vazduhu pomoću uređaja Rapid-E, prvog automatskog detektora čestica zasnovanog na tzv. LIF (eng. laser-induced ﬂuorescence) tehnologiji. LIF tehnologija beleži raspršene fluroscentne obrasce svetlosti i lasera, koji predstavljaju oblik i hemijski otisak čestica. U ovom slučaju se koristi jedan od primera dubokih neuronskih mreža (DNN) koje se najviše oslanjaju na konvolucione slojeve, zbog njihove sposobnosti da obrade skupove podataka koji potiču iz različitih izvora.

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U oba slučaja, predobrada je jedan od najvažnijih koraka, jer su zašumljeni i nestandardni uzorci veoma teški za analizu. Takođe, važno je istaći da svaki problem zahteva drugačije, prilagođeno rešenje, čija složenost odgovara veličini i složenosti skupova podataka i čija tačnost mora opravdati vreme učenja i izvršavanja algoritma.

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Duboko učenje

Sa napretkom tehnologija za varijabilno tretiranje u pogledu sejanja, zalivanja, aplikacije đubriva, pesticida i herbicida, javila se i potreba za sistemima koji tačno, precizno i brzo mogu da odrede varijabilnost zemljišta i identifikuju zone unutar jedne parcele.

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Ovako složen zadatak rešavamo pomoću robotske platforme Agrobot Lala, koja je posebno prilagođena za poljoprivredne primene kao i za ugrađivanje senzora za analizu zemljišta i sistema za uzorkovanje. Predloženi sistem se kreće po parceli i radi uzorkovanje zemljišta i analizu parametara zemljišta kao i georeferenciranje rezultata merenja.

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Celokupan proces je autonoman pošto je moguće prethodno definisati rutu Agrobot Lale na parceli pomoću GPS koordinata i 3D Real-Time-LIDAR sistema.

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Kao rezultat sprovedenih analiza, kreiraju se višeslojne mape, na osnovu kojih poljoprivrednik će biti u mogućnosti da donese ispravne odluke, optimizuje svoju proizvodnju i u potpunosti iskoristi preciznu poljoprivredu u pogledu efikasnosti, prinosa i održivosti poljoprivredne proizvodnje.

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Projekti:

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agROBOfood: Business-Oriented Support to the European Robotics and Agri-food Sector, towards a network of Digital Innovation Hubs in Robotics, H2020-DT-2018-1

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Razvoj uređaja za merenje i mapiranje azota kao najvažnijeg parametra za održivu poljoprivredu, broj rešenja 114-451-2794/2016-03, Pokrajinski sekretarijat za visoko obrazovanje i naučnoistraživačku delatnost, Autonomna pokrajina Vojvodina, Republika Srbija.

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Agrobot za analizu zemljišta na terenu

Cilj BioSensove Grupe za veštačku inteligenciju je da obezbedi sveobuhvatan i precizan uvid u stanje biosistema, poveća efikasnost fenotipizacije i oplemenjivanja biljaka, ponudi bolje razumevanje molekularne biologije i pomogne poljoprivrednicima kroz optimalno i automatsko donešenje odluka. Naše istraživanje počinje od problema i zahteva inovativnu primenu i razvoj algoritama koji su poslednja reč nauke. Algoritmi mašinskog učenja koji se najčešće koriste za probleme kojima se BioSens bavi su stabla odluke, vektori nosači, ridž regresija, spektralno klasterovanje i mnogi drugi, uključujući i posebnu porodicu algoritama mašinskog učenja – duboko učenje, a tamo gde složenost problema prevazilazi postojeće algoritme, istraživači sa Instituta razvijaju nove.

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Primer algoritma koji je razvijen na BioSens Institutu je algoritam regresije težinskim histogramima. Dati algoritam je zasnovan na mehanizmu glasanja, gde uzorci za obuku glasaju za izlaznu vrednost test uzorka. Svaki glas dobija određenu težinu u zavisnosti od sličnosti uzorka koji glasa i uzorka iz test skupa. Pomoću dobijenih glasova, formira se histogram koji de fakto predstavlja funkciju gustine verovatnoće (PDF) izlaza test uzorka, dok matematičko očekivanje PDF-a predstavlja predikovanu vrednost datog uzorka. Ovaj algoritam se pokazao veoma korisnim za problem predikcije prinosa, gde je nadmašio algoritme stabla odluke, vektora nosača i veštačkih neuronskih mreža.

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Još jedan inovativni algoritam koji je razvijen na Institutu BioSens je algoritam integrisane klasterizacije zasnovan na nenegativnoj faktorizaciji matrica. Kombinovanjem individualnih rezultata klasterovanja u ansambl, dokazi se akumuliraju u cilju poboljšanja rezultata klasterizacije.

Individualni rezultati klasteriziacije spojeni su u zajedničku matricu koja se zatim faktorizuje u matrice kodovanja i bazne vektore. Algoritam ima mogućnost da integriše klastere koji potiču iz različitih baza podataka, različitih nivoa predprocesiranja ili poseduju različite podskupove obeležja.
Ovaj algoritam se pokazao kao izuzetno koristan za funkcionalnu genomiku. Dati pristup, zanosvan na nenegativnoj faktorizaciji matrica poseduje mogućnost fuzije različitih izvora podataka i rezultuje klasterima gena sa povećanom funkcionalnošću i pokrivenošću gena.

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Mašinsko učenje

Mašinsko učenje i domenska ekspertiza se često posmatraju kao dva rivalska pristupa u rešavanju problema u raznim oblastima, uključujući poljoprivredu. Međutim, naša strategija je da se koristi najbolje iz oba pristupa. Algoritmi mašinskog učenja daju odlične rezultate pri otkrivanju zavisnosti među podacima, ali uveliko postoje i dobro poznate teorije koje objašnjavaju prirodne procese i koje su već dokazane u praksi. Mi u potpunosti iskorištavamo ovu činjenicu ugrađujući domensko znanje u naše modele. To se obično radi tokom procesa izbora i inženjeringa obeležja. Dobro osmišljena obeležja uvode novi nivo informacija u sistem i usmeravaju algoritme mašinskog učenja ka optimalnim konfiguracijama.

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Jedan primer inženjeringa obeležja, zasnovan na domenskoj ekspertizi, je korišćen pri izboru hibrida kukuruza otpornih na stres u Syngenta izazovu za useve u 2019. godini. Suša je pojava povezana sa dužim periodom vremena bez padavina, praćenog visokim temperaturama i niskom vlažnošću vazduha. Međutim, ne postoji stroga matematička definicija suše i u praksi se koriste razni pokazatelji. Na osnovu dnevnih vrednosti maksimalne i minimalne temperature vazduha, količine padavina i pritiska vodene pare u nekoliko faza tokom vegetacione sezone, dizajnirali smo obeležja koja ukazuju na pojavu suše ili toplotnog stresa na određenoj lokaciji. Među njima su bili maksimalan broj uzastopnih suvih dana, mala količina padavina u određenoj fazi razvoja i deficit vodene pare. U ovom problemu su nam dizajnirana obeležja pomogla da pronađemo hibride otporne na sušu koje treba posejati kako bi se maksimizovao prinos i minimizovao rizik loše žetve, ali se ovaj pristup može primeniti i na druge probleme. Ostali primeri sadrže dizajn meteoroloških obeležja za predikciju koncentracije polena u vazduhu, dizajn elevacionih obeležja za bolju predikciju slivanja nutrijenata na njivama i dizajn obeležja koja opisuju kretanje životinja u predikciji bolesti i inflamacije mlečnih žlezda.

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ANTARES- Centre of Excellence for Advanced Technologies in Sustainable Agriculture and Food Security

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