Center for Product Development

Center for Information Technologies

Project aim: Detection of usurped state-owned parcels, which are not leased at public tenders.
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About the project: The project also includes the detection of illegal burning of crop residues on plots. The mentioned detections are performed by processing satellite images, comparing differences in time series and vegetation indices, based on the algorithm developed by the BioSense Institute. Detections covered all plots on the territory of APV whose area is larger than 1 ha.
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Principal Research Fellow

Vladimir Crnojević

Milijana Vujasinović

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

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

Slobodan Birgermajer

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

Željko Bajić

Vladan Minić is a Manager of the Product Development Center at BioSense Institute. He received an M.Sc. degree in the Faculty of Technical Sciences in 2007. His career started at the Faculty of Technical Sciences, as a member of BioSense Center. When the Center grew into an Institute, he moved to a newly established institution. He participates in many national and international projects within BioSense Institute. His main research interests are wireless sensor networks and image processing, but he also performed the duties of a system administrator. His expertise covers Linux and Windows system; Web development – WordPress, Joomla, PHP, HTML; Programming languages: Python. C/C++ (OpenCV), Matlab; Databases: MySQL, PostgreSQL, MS SQL, SQLite; GIS tools: ArcGIS, QGIS. He also likes hardware and embedded systems.

Vladan Minić

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

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

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

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

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

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

Dr Nataša Ljubičić (F) Research associate, received the Ph.D. degrees from the Faculty of Agriculture, University of Novi Sad, in Serbia in the field of Genetics and Plant Breeding. She holds her Master thesis with the name: ”Multivariate analysis of wheat genotypes grown on meliorated Solonjec soil”, where she investigated different plant responses through genotype/environment interaction caused by different types of soil melioration. Dr Nataša Ljubičić is a Research associate at BioSense Institute, University of Novi Sad, Serbia. Her research interests include using and implementing innovative and smart technology in agricultural and environmental investigations, following genotype/environment interaction, plant response within different mineral nutrition, nutrition, plant stress, phenotypic variability of the plant traits of the agricultural crops. Her investigation involved research from plant scale to large-scale evaluation with the main aim to improve agricultural production. Dr Ljubičić is a member of the EUCARPIA - European Association for Research on Plant Breeding and was involved in several Serbian national projects. She is the author and co-author of more than 50 journal and conference papers on international and national levels and 2 Technical solutions with total citations: 81; h-index: 6 (Google Scholar).

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Nataša Ljubičić

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

National

BioSense has excellent ties with the Serbian Ministry of Agriculture and Provincial Secretariat for Agriculture, Water Management and Forestry of Vojvodina. We are providing a range of solutions that help public administration get a better insight into agricultural production, achieve more efficient control of the subsidies and detect irresponsible use of farmland.

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Our solutions are mainly based on remote sensing and GIS and involve large databases of fields, farmers and acquired data. Services such as crop mapping over the region of interest are developed using Sentinel 2 satellite images and come at a 10m resolution. Crop mapping, in particular, has various purposes, from verification of subsidies to identifying burning of the fields or illegal occupation of state-owned farmland.

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Subsidies are issued for growing particular crops, constructing greenhouses or installing irrigation systems. They are numerous and in-person inspections would require large teams of government officials. Furthermore, it is very time-consuming to check the whole field, which can be tens or hundreds of hectares in size. Using our system, verifications are done remotely, using satellite image processing coupled with advanced machine learning algorithms.

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Similar technology is employed for detection of illegal activities. Traditionally, farmers had often burnt crop residues on their fields to save time and fuel needed for ploughing. This practice was abandoned in the past decades and was declared illegal, as it poses danger to humans and the environment. Also, there have been instances of occupation of state-owned farmland without bidding and paying for the rent. These and other offences are detected using satellite technology and have decreased in occurrence, thus securing safer and more eco-friendly farming, as well as higher revenues for the government.

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Other services include systems set up for various other public stakeholders, most notably for extension services. Extension services give advisory support to farmers and collect large amounts of field data. A dedicated platform based on AgroSense was set up where all the data is stored and visualised using a GIS engine. In this way, thousands of new records, as well as the historical database of field observation were digitised and gathered under one roof.

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Government

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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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

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

Detection of usurped state-owned agricultural land and detection of burning of crop residues on the territory of APV

Centar za razvoj proizvoda

Centar za Informacione Tehnologije

Cilj projekta: Detekcija uzurpiranih parcela u državnom vlasništvu, koja nisu data u zakup na javnim nadmetanjima.
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Opis projekta: Projektom je obuhvaćena i detekcija nezakonitog spaljivanja žetvenih ostataka na parcelama. Pomenute detekcije se vrše obradom satelitskih snimaka, poređenjem razlika u vremenskim serijama i vegetacionim indeksima, a na osnovu algoritma koji razvija BioSens institut. Detekcijama su obuhvaćene sve parcele na teritoriji APV čija je površina veća od 1 ha.
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Naučni savetnik

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.

Slobodan Birgermajer je naučni saradnik u Centru za Senzorske Tehnologije pri Institutu BioSens. Doktorat iz mikrotalasne elektronike odbranio je 2019. godine na Fakultetu Tehničkoih Nauka, Univerziteta u Novom Sadu. Trenutno radi na razvoju i primeni senzora u poljprivredi, prehrambenoj industriji i monitoringu životne sredine, i aktivno je uključena na više međunarodnih projekata iz poziva H2020, kao što su ANTARES, SHEALTY, IPANEMA, i drugi. Takođe, domen istraživanja obuhvata razvoj i fabrikaciju mikrotalasnih pasivnih kola, fotonskih kristala, mikrofludinih senzora, kao i 3D i 4D tehnologiju štampe.

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Željko Bajić je masterirao 2012. godine na Prirodno matematičkom fakultetu u Novom Sadu. Na Biosense institutu radi kao stariji saradnik. Bavi se programiranjem na svim nivoima uglavnom bazirano na Java programskom jeziku i raznim web tehnologijama. Aktivno učestvuje na nekoliko nacionalnih i međunarodnih projekata.

Vladan Minić je menadžer Centra za razvoj proizvoda na Institutu BioSens. Zvanje Master elektrotehnike i računarstva je stekao na Fakultetu tehničkih nauka u Novom Sadu 2007 godine. Karijeru je započeo u BioSens centru pri istom fakultetu. Kada je centar prerastao u Institut, prelazi u novoosnovanu instituciju. Učestvovao je u brojnim domaćim i internacionalnim projektima u okviru BioSensa. Glavna interesovanja su mu vezana za bežične senzorske mreže, obradu slike, ali je obavljao i poslove sistem administratora. Njegova ekspertiza pokriva Linux i Windows sisteme; web razvoj - WordPress, Joomla, PHP, HTML; programski jezici: Python. C/C++ (OpenCV), Matlab; baze podataka: MySQL, PostgreSQL, MS SQL, SQLite; GIS alati: ArcGIS, QGIS. Takođe je zaljubljenik u hardware i embedded sisteme.

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

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.

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.

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.

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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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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Dr Nataša Ljubičić, naučni saradnik, doktorirala je na Poljoprivrednom fakultetu, u Novom Sadu u oblasti Biotehničkih nauka i užoj naučnoj oblasti Genetika i oplemenjivanje biljaka. Magistarsku tezu pod nazivom: ”Multivarijaciona analiza genotipova pšenice gajenih na meliorisanom solonjecu”, kao i diplomski rad iz predmeta Pedologija, odbranila je na Poljoprivrednom fakultetu, u Novom Sadu. Dr Nataša Ljubičić je angažovana u svojstvu naučnog saradnika u okviru Centra za biosisteme, Instituta BioSens u Novom Sadu. Oblast istraživanja obuhvata primenu inovativne i pametne tehnologije u službi povećanja prinosa glavnih poljoprivrenih kultura i pre svega unapređenja poljoprivredne proizvodnje. Istraživanja obuhvataju praćenje fenotipske stabilnosti osobina različitih genotipova pšenice, kukuruza i drugih poljoprivrednih kultura u različitim agroekološkim uslovima, kao i procenu interakcije genotip/spoljna sredina kao različit odgovor biljke na različite uslove sredine. U istraživanjima, fenotipska varijabilnost je praćena merenjem spektralnog odziva biljaka, gde se primenom aktivnih i pasivnih senzora izračunava više od 40 različitih vegetacionih, tj. spektralnih indeksa. Ovakvo udruživanje podataka doprinosi razvoju metoda za procenu stanja biljaka pre vidljivih simptoma i daje smernicu za unapređenje tehnologije proizvodnje, a pre svega maksimalnom iskorišćavanju rodnosti ispitivanih genotipova za područja istih ili sličnih agroekoloških uslova. Pored odabira adekvatnih vegetacionih indeksa, definišu se mere koje pogoduju ili eventualno štete proizvodnji useva za određene lokalitete. Dr Nataša Ljubičić je u dosadašnjem radu bila angažovana u okviru nekoliko projekata Ministarstvo prosvete, nauke i tehnološkog razvoja Republike Srbije, učesnik u međunarodnim recenzijama, kao i član udruženja EUCARPIA (European Association for Research on Plant Breeding). Dr Nataša Ljubičić je u dosadašnjem naučnoistraživačkom radu, kao autor ili koautor, objavila preko 50 naučnih radova u međunarodnim i nacionalnim časopisima koje prema bazi Google Scholar, imaju preko 80 citata u međunarodnim čaopisima i vrednost 6 za Hiršov indeks.

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.

Nacionalni

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

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

BioSens ima odličnu saradnju sa Ministarstvom poljoprivrede Republike Srbije i Pokrajinskim sekretarijatom za poljoprivredu, vodoprivredu i šumarstvo Vojvodine. Za njih smo razvili brojna rešenja koja pomažu javnoj upravi da stekne bolji uvid u poljoprivrednu proizvodnju, postigne efikasniju kontrolu subvencija i otkrije neodgovorno korišćenje poljoprivrednog zemljišta.

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Naša rešenja se uglavnom baziraju na daljinskom istraživanju i GIS-u i uključuju velike baze podataka o poljima, poljoprivrednicima i prikupljenim podacima. Usluge poput mapiranja useva u regionu od interesa razvijaju se pomoću satelitskih snimaka Sentinel 2 i imaju rezoluciju od 10m. Mapiranje useva ima različite svrhe, od verifikacije subvencija do identifikovanja paljenja polja ili ilegalnog zauzimanja državnog poljoprivrednog zemljišta.

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Subvencije se daju za uzgoj određenih useva, izgradnju staklenika ili instaliranje sistema za navodnjavanje. One su brojne, a za inspekciju i proveru pravilnog korišćenja svake ponaosob, potrebni su veliki timovi državnih službenika. Takođe, provera celog polja koje može biti veliko desetine ili stotine hektara zahteva mnogo vremena. Zahvaljujući sistemu koji smo razvili, verifikacije se vrše na daljinu, uz satelitsku obradu slika zajedno sa naprednim algoritmima mašinskog učenja.

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Slična tehnologija se koristi za otkrivanje ilegalnih aktivnosti. Tradicionalno, poljoprivrednici su često spaljivali ostatke useva na svojim poljima kako bi uštedeli vreme i gorivo potrebno za oranje. Ova praksa se ne primenjuje proteklih decenija i proglašena je ilegalnom, jer predstavlja opasnost za ljude i životnu sredinu. Takođe, bilo je slučajeva zauzimanja poljoprivrednog zemljišta u državnom vlasništvu bez nadmetanja i plaćanja zakupnine. Ovi i drugi prekršaji otkriveni su satelitskom tehnologijom i njihov obim je smanjen, čime se osigurava sigurnija i ekološki prihvatljivija poljoprivreda, kao i veći prihodi vlade.

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Ostale usluge uključuju sisteme postavljene za razne druge javne aktere, a posebno za savetodavne usluge. Savetodavne usluge pružaju savetodavnu podršku poljoprivrednicima i prikupljaju velike količine podataka na terenu. Postavljena je namenska platforma zasnovana na AgroSense-u gde se svi podaci čuvaju i vizualizuju pomoću GIS mehanizma. Na taj način su hiljade novih zapisa, kao i istorijska baza podataka posmatranja na terenu digitalizovane i prikupljene pod jednim krovom.

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Saradnja sa Državnom upravom

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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Detection of usurped state-owned agricultural land and detection of burning of crop residues on the territory of APV

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