Center for Biosystems

Center for Information Technologies

Project aim: DRAGON project aims to contribute to overcoming main challenges in the field of precision agriculture (PA) – low rate of adoption of PA technologies and practices, especially concerning the “big data in agriculture”. BioSense Institute is the project coordinator, with the Wageningen University from the Netherlands and the Agri-Epi Centre from Scotland as project partners.
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About the project: One of the main project goals is advancement in Science &amp; Technology skills of the BioSense Institute, firstly in the area of multiscale data analytics from various sources such as satellites, mobile phones, devices related to the internet of things as well as data in the field of phenomics, genomics and metagenomics which comprise the data on morphological and genetic characteristics of plants and bacteria from soil.
Project also focuses on improving the communication of new research data and findings to the end users, primarily farmers, small and medium size agricultural companies as well as policy makers and other public institutions.
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Project activities are focused on the young researchers from BioSense Institute, who participate in capacity-building short term visits to the partners institutions. Different types of seminars and workshops for researchers, students and the end users are going to take place in Serbia as well.
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Senior research associate in the field of natural/mathematical sciences - biology

Ivana Gađanski

Maksim Lalić is a research trainee at the BioSense Institute and a PhD student at the Faculty of Organizational Sciences, University of Belgrade. He finished his BSc (2017) and MSc (2018) studies in Electrical and Computer Engineering at the Faculty of Technical Sciences, University of Novi Sad. After finished MSc studies, he has been working as a teaching assistant engaged in subjects related to data analysis and databases areas. He is interested in applications of machine learning, operations research and artificial intelligence in the development of agent systems and decision support systems.

Maksim Lalić

Marija Kopanja is a PhD student of Informatics at Faculty of Sciences, University of Novi Sad. At the same faculty, she has previously finished bachelor and master studies in Applied mathematics during which she gained knowledge in the field of finance, as well as in the field of the numerical optimization and machine learning, that represent main areas of her scientific research. She attended ECMI Mathematical Modelling Week in 2018. During the studies she has been financially supported by Ministry of Education, Science and Technological Development of the Republic of Serbia. She received her master’s degree with the thesis about analysis of machine learning methods for cost-sensitive classification problems and their application on imbalanced data within medicine. Besides machine learning, her main research interest is use of the explainable artificial intelligence and deep learning, for application in agriculture and medicine. She is actively involved in several projects, including DRAGON.

Marija Kopanja

Milica Brkić is a researcher at the Biosense Institute and a doctoral student of Informatics with a Bachelor in Mathematics and a Master diploma in Data science obtained at the Faculty of Sciences, University of Novi Sad, Serbia. During her doctoral studies, she spent the summer semester at the University of Coimbra in Portugal where she studied as a scholarship holder of the Erasmus+ Exchange Program. Since November 2018 Milica has been working as a teaching assistant at the Faculty of Sciences, she is responsible for the Programming for data science course. Her research focuses on the application of machine learning in agriculture. She was a part 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 the Syngenta Crop Challenge in Analytics 2019.

Milica Brkić

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

Stefan Jarić is employed at the Center for Sensing Technologies as a Research Assistant and a final-year PhD student in materials physics at the Department of Physics, Faculty of Sciences at the University of Novi Sad. Stefan works on the development and research of new solutions in the field of biosensors based on graphene field effect transistors and specific synthetic receptors (aptamers) for the detection of various analytes in the area of environmental protection (toxins) and medicine (biomarkers). His expertise is developing in the direction of micro and nanofabrication of sensor platforms (photolithography, physical vapor deposition of materials), characterization of materials (Raman spectroscopy, atomic force microscopy) and electrical characteristics of biosensors.

Stefan Jarić

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

Vladan Filipović is a Junior researcher in the field of ICT within the Center for Information Technologies at the BioSense Institute. He has received BSc (2020) degree in Electrical and Computer Engineering, from the Department of Power, Electronic and Telecommunication Engineering, Signal Processing module, Faculty of Technical Sciences, University of Novi Sad. His research focus is study of digital image processing and machine learning techniques for applications in agriculture. His current research interests are primarily directed towards segmentation and classification of RGB and hyperspectral images.

Vladan Filipović

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

Predrag Lugonja is a researcher at BioSense Institute. He graduated from the Faculty of Technical Sciences, University of Novi Sad, Department of Power, Electronic and Telecommunication Engineering. He is currently in the final year of his PhD studies at the Faculty of Technical Sciences. He has been working in the fields of GIS and remote sensing

Predrag Lugonja

Oskar Marko, Ph.D. is the Head of the Center for Information Technologies and the Assistant Director for Innovation and Cooperation with the Economy at the BioSense Institute. 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

Nina Pajević is a Junior researcher in the field of ICT within the Center for Information Technologies at the BioSense Institute. She has received a BSc (2020) degree in Electrical and Computer Engineering, from the Department of Power, Electronic and Telecommunication Engineering, Signal Processing module, Faculty of Technical Sciences, University of Novi Sad. Her research focus is the study of machine learning techniques for applications in agriculture. Her current research interests are primarily directed towards detecting irrigated surfaces based on satellite images.

Nina Pajević

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

Nastasija Grujić is a junior research assistant at the BioSense Institute and a PhD student in Geoinformatics at the University of Novi Sad, Faculty of Technical Sciences. She finished her MSc (2018) and BSc (2017) studies in Geodesy and Geomatics Engineering. During the studies, she received two Dositeja awards for the top 800 and 400 students in the country (school years 2016/2017 and 2017/2018, respectively). In late 2018, she enrolled in PhD studies and started working at the BioSense Institute. During the studies, she was twice a guest researcher at the University of Wageningen, where she got acquainted with modeling complex and stochastic systems through agent-based simulation. Her particular research interest span a wide range of data analytics, machine learning, and geoinformatics which are used for better understanding and simulating the complex spatial systems through individual and agent-based simulations. She is involved in various national and international projects, including H2020 projects ANTARES, DRAGON, and BESTMAP.

Nastasija Grujić

Mirjana Radulović is a Junior Research Assistant at the BioSense Institute with interests in remote sensing, geoinformatics, water resource management, agriculture, and the environment. She is currently enrolled as a Ph.D. student of Geosciences at the Faculty of Sciences, the University of Novi Sad where she also received her BSc (2017) and MSc (2018) in the field of hydrology and geoinformatics. During master studies, she spent five months at the Palacký University in Olomouc (Czech Republic) where she studied as a scholarship holder of the Erasmus+ Exchange Program. In March 2019 she joined the BioSense Institute where she works on applying geographical information systems and remote sensing tehnology for monitoring of irrigation, drought monitoring, crop monitoring, land use classification, groundwater monitoring, etc. She participates in several national and international projects related to these topics. Mirjana authored and co-authored several scientific papers and attended several scientific conferences.

Mirjana Radulović

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 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 data analytics in the field of agrometeorology and biometeorology. 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 20 journal articles, 4 conference papers and 1 book chapter.

Gordan Mimić

Dragana Blagojević obtained BSc (2017) and MSc (2018) degrees in geography at the Faculty of Sciences in Novi Sad with a focus on geographic information systems and their use in the hydrological and geomorphological analysis. In October 2018, she enrolled in Ph.D. studies in Geosciences at the same faculty. She spent the summer semester of the master studies at the Palacký University in Olomouc (UPOL) in the Czech Republic, as a scholarship holder of the Erasmus+ Exchange Program where she studied at the Department of Geoinformatics. In January 2019 she joined BioSense Institute where she works as a Junior Research Assistant in the Centre for Information Technologies. Her research area is in the field of remote sensing, precision agriculture, yield monitoring, and mapping as well as filed work analyses. Her research aim is an integration of these techniques to get complete and precise information about filed, crops, terrain, and soil characteristics.

Dragana Blagojević

Dimitrije Stefanović is a Junior researcher in the field of ICT within the Center for Information Technologies at the BioSense Institute. He has received BSc (2020) degree in Electrical and Computer Engineering, from the Department of Power, Electronic and Telecommunication Engineering, Signal Processing module, Faculty of Technical Sciences, University of Novi Sad. His research focus is study of digital image processing and machine learning techniques for applications in agriculture. His current research interests are primarily directed towards denoising of pollen particle holograms and various reconstruction methods in digital holography.

Dimitrije Stefanović

Dejan Pavlović is a researcher at BioSense Institute and a PhD student of System Control and Signal Processing at School of Electrical Engineering, University of Belgrade. His research interests cover signal processing and machine learning application, particularly focused on the field of precision agriculture. Dejan received his bachelor’s degree in Biomedical Engineering from the Faculty of Technical Sciences, University of Novi Sad and master’s degree in Electrical and Computer Engineering from School of Electrical Engineering, University of Belgrade. He is currently working in the field of data analytics developing an algorithm for the classification of multiple-behaviour patterns in cattle using Convolutional Neural Networks. He is actively involved in many projects, including Horizon 2020 projects, Antares and Dragon.

Dejan Pavlović

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

Teodora Knežić joined BioSense Institute as Junior Researcher in the Center for Biosystems. Teodora has a bachelor's and master's degrees in Molecular Biology and she is currently a Ph.D. student of Molecular Biology at the Faculty of Sciences, University of Novi Sad, Serbia, with Dr Ivana Gađanski as the mentor. During her studies she volunteered in the Laboratory for Biochemistry and Molecular Biology at the Faculty of Sciences where was involved in the analyses of gene expression of the European corn borer using techniques for RNA isolation, gel electrophoresis, cDNA synthesis, end-point and qPCR analysis. Also, at the Institute for Nuclear Sciences Vinča she was trained to work with human cancer cell cultures, perform viability assays and flow cytometry. Teodora has just completed a 3 month specialization practice in BioSense as well. Her main research interests are biosensor development and molecular techniques for pathogen detection, cellular agriculture and development of lab-on-a-chip platforms.

Teodora Knežić

Mila Djisalov has a BSc in Biochemistry and a Master’s degree in Molecular Biology. She is currently a Ph.D. Student of Biochemistry at the Faculty of Sciences in Novi Sad and a Junior Researcher in BioSense Institute. Her main research interests are biosensor development and molecular tools for pathogen detection, cellular agriculture, and the development of lab-on-a-chip platforms. Mila is also one of the co-founders of the student group The BioSense Alt. Protein Project formed at the BioSense Institute.

MIla Đisalov

Dr Ljiljana Šašič Zorić is a senior researcher at the BioSense Institute with expertise in genetics. Ljiljana received PhD degree in biology in 2018 from Faculty of Sciences, University of Novi Sad. She is particularly oriented on application of mitochondrial and nuclear sequence based molecular markers in genetic analyses, identification and delimitation of European insect species. Since 2016, she has been collaborating with DNA laboratory of Finnish museum of Natural History, University of Helsinki where she gained additional experience in molecular taxonomy of hoverflies. In 2017, her research concerning influence of endosymbiotic bacteria on evolutionary dynamic of mitochondrial genes was granted by the Entomological Society of Helsinki and the Societas pro Fauna et Flora Fennica. Her project "Genomic approach in hoverfly biodiversity assessment - next-generation DNA barcoding" was awarded the 100-year anniversary grant of the Entomological Society of Helsinki in 2019. Ljiljana is informal member of the genetic research team at Department of Biology and Ecology, Faculty of Sciences, Novi Sad, as well as coordinator of research activities at the Valentis Center for Behavioral Research, Bačka Palanka.

Ljiljana Šašić Zorić

Ljiljana Janjusevic PhD is Research Associate at the BioSens Institute, University of Novi Sad, Serbia. Bechelor, master and doctoral studies were completed at the Department of Biology and Ecology, Faculty of Sciences, University of Novi Sad. During the doctoral studies, the focus of the research was on examining the biological activities and chemical composition of macrofungi as well as the essntial oils of some plants. Research at the Institute focuses on the development of sensors and molecular techniques for pathogen detection, biomolecular engineering and cell agriculture.

Ljiljana Janjušević

Ivana Gadjanski PhD is Research Associate Professor at BioSense Institute, University of Novi Sad, Serbia. She graduated from the Faculty of Biology, University of Belgrade as the 1st in the class (2003). She obtained PhD in Neuroscience (summa cum laude -oral presentation) from Georg-August University &amp; Max Planck Institute for Biophysical Chemistry, Goettingen, Germany (2007) and worked as a postdoctorate researcher at Ludwig-Maximillian University, Munich, Germany. Ivana then turned to the bioengineering field and spent a year as a Fulbright Visiting Scholar at Columbia University, USA - Lab for Stem cells and Tissue Engineering (Head: prof. Gordana Vunjak-Novakovic). In BioSense, she is moving further into the cellular agriculture field - developing solutions for cultivated meat. She also works on biosensing for agrifood, biomedicine and environmental monitoring. Ivana is a grantee of the Good Food Institute and coordinator of Horizon2020 projects DRAGON GA 810755 and IPANEMA GA 872662.

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Isidora Simović is a senior researcher at BioSense Institute. She did her PhD at the Faculty of Forestry in Belgrade, observing morphology, phenology, and anatomy of trees in various habitats. She is currently working on application of remote sensing techniques in the monitoring of climate and anthropogenic impacts on natural and urban ecosystems.

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Isidora Simović

Bojana Ivošević is working on utilizing Unmanned Aerial Vehicles (UAV) for efficient monitoring of croplands, freshwaters, and terrains. She hails from a background in biology and ecology. She initiated her PhD in Kyungpook National University, South Korea, where she gained 2 years of experience with UAVs. She continued to pursue her field of interest at the Faculty of Sciences, University of Novi Sad, Serbia. Her current research focus is to implement UAV technology in precision agriculture to calculate vegetation indices important for agricultural crop yield production, disease detection and crop management. She is also interested in enforcing UAVs for animal detection and counting, monitoring algae bloom, land cover classification, and seasonal effects on the ecosystems.

Bojana Ivošević

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This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 810775. 
                      

Horizon 2020

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

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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<img class="alignnone wp-image-1474 size-full" src="https://api.biosens.rs/wp-content/uploads/2021/06/39-1.jpg" alt="" width="1187" height="1028" />

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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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<img class="alignnone wp-image-1476 " src="https://api.biosens.rs/wp-content/uploads/2021/06/40-1024x768.jpg" alt="" width="351" height="263" /><img class="alignnone wp-image-1477 " src="https://api.biosens.rs/wp-content/uploads/2021/06/41-1024x768.jpg" alt="" width="351" height="263" />

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

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

DRAGON -Data Driven Precision Agriculture Services and Skill Acquisition

Centar za Biosisteme

Centar za Informacione Tehnologije

Cilj projekta: Projekat DRAGON ima za cilj rešavanje glavnih izazova precizne poljoprivrede (PP) – niske stope prihvatanja i primene PP tehnologija u praksi, naročito tzv. big data u poljoprivredi. Institut BioSens je koordinator projekta, a partneri su Vaheningen Univerzitet iz Holandije i AgriEpi Centar iz Škotske.
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O projektu: Jedan od primarnih ciljeva projekta je unapređenje istraživačkih kapaciteta Instituta BioSens prvenstveno što se tiče analize multiskalarnih podataka iz različitih izvora kao što su sateliti, mobilni telefoni, uređaji u okviru tzv. interneta stvari, kao i podaci u oblasti fenomike, genomike i metagenomike, tj. podaci o morfološkim i genetičkim osobinama biljaka i bakterija iz zemljišta. 
Drugi važan cilj projekta je i poboljšana sposobnost komunikacije novih naučnih saznanja i podataka krajnjim korisnicima, tj. zemljoradnicima, malim i velikim kompanijama u poljoprivredi, kao i državnim i drugim zvaničnim organizacijama.
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Aktivnosti projekta su prvenstveno usmerene ka mladim istraživačima BioSens instituta, koji će učestvovati na stručnim boravcima i usavršavanjima na partnerskim institucijama. Predviđeni su različiti seminari i treninzi u Srbiji, kako za istraživače i studente, tako i za potencijalne krajnje korisnike PP tehnologija.
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 Viši naučni saradnik u oblasti prirodno-matematičke nauke - biologija

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Maksim Lalić je istraživač pripravnik na institutu BioSens i student doktorskih studija na Fakultetu organizacionih nauka, Univerziteta u Beogradu. Osnovne i master akademske studije iz oblasti računarstva je završio 2017. i 2018. godine na Fakultetu tehničkih nauka u Novom Sadu. Nakon završenih master studija, ostaje zaposlen na Fakultetu tehničkih nauka kao asistent sa angažmanom na predmetima iz oblasti analize podataka i baza podataka. Interesuje se za primene metoda mašinskog učenja, operacionih istraživanja i veštačke inteligencije u razvoju agentskih sistema i sistema za podršku u odlučivanju.

Marija Kopanja je student doktorskih studija Informatike na Prirodno-matematičkom fakultetu, Univerziteta u Novom Sadu. Na istom fakultetu je prethodno završila osnovne i master studije Primenjene matematike tokom kojih je stekla znanje iz oblasti finansija, ali i numeričke optimizacije i mašinskog učenja koje predstavljaju glavne oblasti njenog interesovanja. Učestvovala je na ECMI Matematičkoj nedelji modeliranja 2018.godine. Tokom studija je bila stipendista Ministarstva obrazovanja, nauke i tehnološkog razvoja, Republike Srbije. Master studije je završila na temi analize metoda mašinskog učenja za rešavanje klasifikacionih problema osetljivih na cenu greške i njihove primene na nebalansirane podatke u oblasti medicine. Pored mašinkog učenja, njeno istraživačko interesovanje je usmereno na objašnjivu veštačku inteligenciju i duboko učenje, sa primenom u poljoprivredi i medicini. Trenutno je aktivni učesnik nekoliko projekata, uključujući DRAGON.

Milica Brkić je istraživač na Institutu Biosens i student doktorskih studija Informatike. Završila je osnovne studije matematike i master studije nauke o podacima na Prirodno-matematičkom fakultetu (PMF) u Novom Sadu. Tokom letnjeg semestra druge godine doktorskih studija kao stipendista Erasmus + programa razmene provela je na Univerzitetu u Koimbri, Portugal. Od novembra 2018. radi kao asistent u nastavi na master kursu Programiranje u nauci o podacima na PMF-u. Fokus njenog istraživanja je primena mašinskog učenja u poljoprivredi. Milica je bila deo tim BioSensa koji je razvio novi algoritam za klasifikaciju hibrida kukuruza koji si otporni na sušu i toplotni stress, i za koji su osvojili treću nagradu na Sindžentinom “Izazovu za useve” 2019. godine.

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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Stefan Jarić je zaposlen u Centru za senzorske tehnologije kao istraživač-saradnik i student završne godine doktorskih studija fizike materijala na Departmanu za fiziku, Prirodno-matematičkog fakulteta u Novom Sadu. Stefan se bavi razvojem i istraživanjem novih rešenja na polju biosenzora zasnovanih na grafenskim tranzistorima sa efektom polja i specifičnih sintetičkih receptora (aptamera) za detekciju raznih analita iz sfere zaštite životne sredine (toksini) i medicine (biomarkeri). Njegova ekpertiza se razvija u pravcu mikro i nanofabrikacije senzorskih platformi (fotolitografija, fizičko naparavanje materijala), karakterizacije materijala (Ramanova spektroskopija, mikroskopija atomskih sila) i električnih karakteristika biosenzora.

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.

Vladan Filipović zaposlen je u Centru za informacione tehnologije Instituta BioSens, na poziciji Mlađi istraživač u oblasti informaciono-komunikacionih tehnologija. Završio je osnovne studije na Departmanu za energetiku, elektroniku i telekomunikacije, smer Obrada signala, Fakulteta tehničkih nauka, Univerziteta u Novom Sadu; Diplomirani inženjer elektrotehnike i računarstva je od 2020. godine. Prevashodno je usmeren ka istraživanju u oblasti digitalne obrade slike i primeni algoritama mašinskog učenja u poljoprivredi. Posebno je zainteresovan za segmentaciju i klasifikaciju RGB i hiperspektralnih slika.

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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Predrag Lugonja je istraživac na Institutu BioSens. Diplomirao je na Departmanu za Energetiku, elektroniku i telekomunikacije Fakulteta tehničkih nauka Univerziteta u Novom Sadu. Student je završne godine doktorksih studia na Fakulteta tehničkih nauka Univerziteta u Novom Sad. Trenutno se bavi istraživanjem na poljima GIS i podataka koje su dobijeni daljinskom detekcijom.

Dr Oskar Marko je rukovodilac Centra za informacione tehnologije i pomoćnik direktora za inovacije i saradnju sa privredom. 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.

Nina Pajević zaposlena je u Centru za informacione tehnologije Instituta BioSense, na poziciji Mlađi istraživač u oblasti informaciono-komunikacionih tehnologija. Završila je osnovne studije na Departmanu za energetiku, elektroniku i telekomunikacije, smer Obrada signala, Fakulteta tehničkih nauka, Univerziteta u Novom Sadu; Diplomirani inženjer elektrotehnike i računarstva je od 2020. godine. Prevashodno je usmerena ka istraživanju i primeni algoritama dubokog učenja u poljoprivredi. Trenutno radi na detekciji navodnjavanih površina na osnovu satelitskih slika.

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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.

Nastasija Grujić je istraživač pripravnik na Institutu BioSens i student doktorskih studija geoinformatike na Univerzitetu u Novom Sadu, na Fakultetu tehničkih nauka. Završila je master (2018) i osnovne akademske studije (2017) u oblasti geodezije i geomatike. Tokom studija je dobila dve Dositejeve nagrade za najboljih 800 i 400 studenata u državi (školske godine 2016/2017 i 2017/2018 redom). Na kraju 2018. godine je upisala doktorske studije i počela je raditi na Institutu BioSens. Tokom studija je bila dva puta gostujući istraživač na Univerzitetu u Vageningenu, gde se upoznala sa agentskim modelovanjem kompleksnih i stohastičkih sistema. Njena interesovanja uključuju širok spektar analitike podataka, mašinskog učenja i geoinformatike koji se koriste za bolje razumevanje i simulaciju kompleksnih prostornih sistema pomoću agentskih simulacija. Učesnik je na raznim nacionalnim i internacionalnim projektima, uključujući i H2020 projekte ANTARES, DRAGON i BESTMAP.

Mirjana Radulović je istraživač-pripravnik na Institutu Biosens čija interesovanja su okrenuta ka daljinskoj detekciji, geoinformatici, upravljanju vodnim resursima, poljoprivredi i životnoj sredini. Ona je trenutno student doktorskih studija geonauka na Prirodno-matematičkom fakultetu, Univerzitetu u Novom Sadu gde je takođe stekla diplomu osnovnih (2017) i master studija (2018) u oblasti hidrologije i geoinformatike. Tokom master studija pet meseci provela je na Univerzitetu Palacký u Olomoucu (Republika Češka), gde je studirala kao stipendista Erasmus + programa razmene. U martu 2019. godine pridružuje se Institutu Biosens gde radi na primeni geografskih informacionih sistema i tehnologija daljinske detekcije za praćenje navodnjavanih površina, suše, stanja useva, klasifikaciju iskoristivosti zemljišta, praćenje podzemnih voda itd. Učestvuje na nekoliko nacionalnih i internacionalnih projekata vezanih za navedene teme. Bila je autor i koautor na nekoliko radova i prisustvovala je na nekoliko naučnih konferencija.

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 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 analitikom podataka u oblasti agrometeorologije i biometeorologije. Predvodio je tim BioSensa koji je razvio novi algoritam za klasifikaciju hibrida kukuruza koji si 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 20 naučnih radova, 4 konferencijska rada i 1 poglavlja u knjizi.

Dragana Blagojević je završila osnovne (2017) i master (2018) akademske studije smer geograf-geoinformatičar na Departmanu za geografiju, turizam i hotelijerstvo na Prirodno-matematičkom fakultetu u Novom Sadu. Njena istraživanja za završne radove su obuhvatala upotrebu geografskih informacionih sistema u analizi hidroloških i geomorfoloških procesa u prirodi. Na istom fakultetu, u oktobru 2018. godine upisuje doktorske studije, smer – Geonauke. Letnji semestar master studija provela je u Češkoj na Departmanu za Geoinformatiku na Palacki Univerzitetu u Olomoucu kao stipendista Erasmus+ programa razmene studenata. U januaru 2018. godine počinje da radi na Institutu BioSens gde je zaposlena kao istraživač-pripravnik u okviru Centra za informacione tehnologije. Njeni domeni istraživanja su daljinska detekcija, precizna poljoprivreda, monitoring i mapiranje prinosa, kao i analiza podataka dobijenih na terenu. Cilj njenog istraživanja je integracija ovih tehnika kako bi se dobila kompletna i precizna informacija o stanju poljoprivredne parcele, useva, fizičkih karakteristika terena i zemljišta.

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Dimitrije Stefanović zaposlen je u Centru za informacione tehnologije Instituta BioSens, na poziciji Mlađi istraživač u oblasti informaciono-komunikacionih tehnologija. Završio je osnovne studije na Departmanu za energetiku, elektroniku i telekomunikacije, smer Obrada signala, Fakulteta tehničkih nauka, Univerziteta u Novom Sadu; Diplomirani inženjer elektrotehnike i računarstva je od 2020. godine. Prevashodno je usmeren ka istraživanju u oblasti digitalne obrade slike i primeni algoritama dubokog učenja u poljoprivredi. Posebno je zainteresovan za uklanjanje šuma iz holograma čestica polena i različite metode rekonstrukcije u digitalnoj holografiji.

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Dejan Pavlović je istraživač na Institutu Biosens i student doktorskih studija na Elektrotehničkom fakultetu, Univerziteta u Beogradu. Njegovo primarno interesovanje obuhvata obradu signala i primenu mašinskog učenja u poljoprivredi. Dejan je stekao zvanje diplomiranog inženjera biomedicinskog inženjerstva na Fakultetu tehničkih nauka, Univerziteta u Novom Sadu i zvanje master inženjera elektrotehnike i računarstva na Elektrotehničkom fakultetu, Univerziteta u Beogradu. Trenutno radi na razvoju algoritma za klasifikaciju različitih aktivnosti kod krava primenom Konvolucionih neuralnih mreža. Učestvuje u realizaciji nekoliko nacionalnih i internacionalnih projekata uključujući Horizon 2020 projekte, Antares i Dragon.

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.

Teodora Knežić je zaposlena kao istraživač-pripravnik na Institutu BioSens, u Centru za biosisteme. Teodora je završila osnovne i master akademske studije molekularne biologije i trenutno pohađa doktorske akademske studije molekularne biologije na Prirodno-matematičkom fakultetu Univerziteta u Novom Sadu, pod mentorstvom dr Ivane Gađanski. Tokom svojih studija volontirala je u Laboratoriji za biohemiju i molekularnu biologiju na Prirodno-matematičkom fakultetu gde je vršila analizu genske ekspresije kukuruznog plamenca koristeći metode za izolaciju RNK, gel elektroforezu, sintezu cDNK, end-point i qPCR analizu. Takođe, na Institutu za nuklearne nauke Vinča obučena je za rad sa humanim ćelijskim linijama, izvođenje eseja vijabilnosti i protočnu citometriju. Teodora je završila i tromesečno fakultativno stručno usavršavanje na Institutu BioSens. Njen glavni fokus istraživanja je razvoj biosenzora i molekularnih tehnika za detekciju patogena, ćelijska poljoprivreda i razvoj lab-on-a-chip platformi.

Mila Đisalov je diplomirani biohemičar i master molekularne biologije. Trenutno je student doktorskih studija biohemije na Prirodno-matematičkom fakultetu u Novom Sadu i istraživač-pripravnik na Institutu BioSens. Njena glavna istraživačka interesovanja vezana su za razvoj biosenzora i molekularnih alata za detekciju patogena, ćelijska poljoprivreda i razvoj laboratorija na čipu. Mila je takođe jedan od osnivača studentske grupe The BioSense Alt. Protein Project, osnovane na Institutu BioSens.

Mila Đisalov

Dr Ljiljana Šašič Zorić je istraživač na BioSense Institutu sa stručnim znanjem u oblasti genetike. Ljiljana je doktorirala 2018. godine na Departmanu za biologiju i ekologiju, na Prirodno-matematičkom fakultetu Univerziteta u Novom Sadu. Posebno je orijentisana na primenu sekvenci mitohondrijalnih i nuklearnih molekularnih markera u genetičkim analizama, identifikaciji i razgraničavanju Evropskih vrsta insekata. Od 2016. godine sarađuje sa DNK laboratorijom Finskog Prirodnjačkog muzeja, Univerziteta u Helsinkiju, gde je stekla dodatno iskustvo u molekularnoj taksonomiji osolikih muva. Tokom 2017. godine, njeno istraživanje o uticaju endosimbiotskih bakterija na evolucionu dinamiku mitohondrijalnih gena stipendirano je od strane Entomološkog društva u Helsinkiju i Društva za zaštitutu flore i faune Finske. Njen projekat „Genomski pristup u proceni biodiverziteta osolikih muva - DNK barkoding nove generacije“ nagrađen je stogodišnjom jubilarnom stipendijom Entomološkog društva iz Helsinkija za 2019. godinu. Ljiljana je neformalni član tima za genetička istraživanja na Departmanu za biologiju i ekologiju, Prirodno-matematičkog fakulteta u Novom Sadu, kao i koordinator istraživačkih aktivnosti u Centru za bihejvioralna istraživanja Valentis, Bačka Palanka.

Ljiljana Janjušević PhD je naučni saradnik na BioSens institutu, Unirzitet u Novom Sadu, R. Srbija. Diplomske, master i doktorske studije završila je na Departmanu za biologiju i ekoloiju Prirodno-matmatičkog fakulteta Univerziteta u Novom Sadu. Tokom doktorskih studija fokus istraživanja je bio usmeren na ispitivanje biološke aktivnosti i hemijskog sastava makrogljiva kao i etarskih ulja nekih biljaka. Istraživanja na Institutu su usmerena na razvoj senzora i molekularnih tehnika za detekkciju patogena, biomoleularni inžnjering i ćelijsku poljoprivredu.

Dr Ivana Gađanski je viši naučni saradnik na Institutu Biosens, Univerzitet u Novom Sadu, Srbija. Diplomirala je na Biološkom fakultetu Univerziteta u Beogradu kao prva u klasi (2003). Doktorirala je iz neuronauka (ocena summa cum laude – usmena odbrana) na Georg-August Univerzitetu i Institutu za biofizičku hemiju Maks Plank, Getingen, Nemačka (2007) i radila na posledoktorskim studijama na Univerzitetu Ludvig-Maksimilijian, Minhen, Nemačka. Ivana se zatim posvetila oblasti bioinženjerstva i provela godinu dana kao Fulbrajt stipendista – gostujući istraživač na Univerzitetu Kolumbija, Njujork, SAD u Laboratoriji za matične ćelije i tkivno inženjerstvo (rukovodilac: prof. Gordana Vunjak-Novaković). U BioSensu radi na bioinženjerstvu u oblasti ćelijske poljoprivrede - razvija rešenja za kultivisano meso. Takođe radi na razvoju biosenzora za monitoring u oblasti poljoprivrede, biomedicine i zaštite životne sredine. Ivana je stipendista Instituta za dobru hranu i koordinator Horizont2020 projekata DRAGON GA 810755 i IPANEMA GA 872662.

Isidora Simović je naučni saradnik na BioSens Institutu. Doktorila je na Šumarskom fakultetu u Beogradu radeći na praćenju morfologije, fenologije I anatomije drveća u različitim sredinama. Trenutno radi na primeni daljinske detekcije u praćenju klimatskihi antropogenih uticaja na prirodne i urbane ekosisteme.

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Bojana Ivošević se bavi upotrebom bespilotnih letelica za efikasno nadgledanje ratarskih kultura, slatkih voda i životne sredine. Ona je po struci biolog i ekolog. Doktorske studije je upisala na Kyungpook Nacionalnom Univerzitetu u Južnoj Koreji, gde je stekla dve godine iskustva rada na bespilotnim letelicama. Nastavila je studije na Prirodno-matematičkom fakultetu, Univerzitetu u Novom Sadu. Njen trenutni fokus istraživanja je primena bespilotnih letelica u preciznoj poljoprivredi za izračunavanje vegetacijskih indeksa važnih za procenu prinosa, detekciju bolesti i upravljanje usevima. Takođe je zainteresovana za primenu dronova u detekciji i prebrojavanju životinjskih vrsta, praćenju cvetanja algi, klasifikaciju zemljišnog pokrivača i sezonskih efekata na ekosisteme.

 Ovaj projekat je finansiran od strane Evropske Unije, iz programa za Istraživanje i Inovacije- Horizont 2020, pod sporazumom o grantu broj 810775. 
                     

Horizont 2020

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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<img class="alignnone wp-image-1853 size-full" src="https://api.biosens.rs/wp-content/uploads/2021/06/slika5.png" alt="" width="775" height="444" />

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

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

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

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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DRAGON – Data Driven Precision Agriculture Services and Skill Acquisition

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