Center for Innovation and Business Development

Center for Information Technologies

Project aim: CYBELE’s aim is to generate innovation and create value in the domains of agri-food by implementing Precision Agriculture (PA) and Precision Livestock Farming (PLF) methods, and to empower capacity building within the industrial and research communities associated with these domains.
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About the project: CYBELE generates innovation and create value in the domain of agri-food, and its verticals in the sub-domains of PA and PLF in specific, as demonstrated by the real-life industrial cases to be supported, empowering capacity building within the industrial and research community. Since agriculture is a high volume business with low operational efficiency, CYBELE aspires at demonstrating how the convergence of HPC, Big Data, Cloud Computing and the IoT can revolutionize farming, reduce scarcity and increase food supply, bringing social, economic, and environmental benefits. CYBELE intends to safeguard that stakeholders have integrated, unmediated access to a vast amount of large scale datasets of diverse types from a variety of sources, and they are capable of generating value and extracting insights, by providing secure and unmediated access to large-scale HPC infrastructures supporting data discovery, processing, combination and visualization services, solving challenges modelled as mathematical algorithms requiring high computing power. CYBELE develops large scale HPC-enabled test beds and delivers a distributed big data management architecture and a data management strategy providing 1) integrated, unmediated access to large scale datasets of diverse types from a multitude of distributed data sources, 2) a data and service driven virtual HPC-enabled environment supporting the execution of multi-parametric agri-food related impact model experiments, optimizing the features of processing large scale datasets and 3) a bouquet of domain specific and generic services on top of the virtual research environment facilitating the elicitation of knowledge from big agri-food related data, addressing the issue of increasing responsiveness and empowering automation-assisted decision making, empowering the stakeholders to use resources in a more environmentally responsible manner, improve sourcing decisions, and implement circular-economy solutions in the food chain.

Milica Trajković

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

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

Ms. Isidora Stojačić (F) works as a Project Manager at the Business Development Department at BioSense Institute. She obtained her bachelor’s degree at the University of Novi Sad, Faculty of Economics, and her master’s degree at the University Cattolica del Sacro Cuore, Faculty of Agriculture and Life
Sciences and at the Technical University of Munich, Faculty of Life Sciences. She has a few years of experience in project management, research, and project proposal writing. She has been involved in various topics, mostly related to Responsible Research and Innovation, co-creation, social design and implementation, market and business exploitation and multi-actor engagement strategies. She has been involved in Horizon2020, ERASMUS+ and National projects.

Isidora Stojačić

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

Željana Grbović

Prof. Vladimir Crnojević is Director of the BioSense Institute which focuses on R&amp;D in ICT for agrifood sector at various levels: from micro and nanotechnology sensors, through Internet of Things, agrirobotics, remote sensing, to big data, machine learning and artificial intelligence. Prof. Crnojević is a Full Professor in Computer Sciences at the University of Novi Sad, Serbia and Associate Professor Extraordinary at the Department of Mathematical Science, Stellenbosch University, South Africa. He studied Electrical Engineering (EE) and obtained his PhD in Image Processing in 2004. He acted as Director of EE Department at the University of Novi Sad from 2012-2015, and in 2015 founded the BioSense Institute – the regional leader in EU (FP7 and H2020) projects, including flagship projects such as ANTARES, IoF2020 – Internet of Food and Farm 2020 and SmartAgriHubs. BioSense Institute has strong cooperation with industry and has run a significant number of accelerator programs through which more than 200 SMEs in AgTech were funded. He led the team of BioSense researchers who won the 1st Prize at the 2017 Syngenta Crop Challenge with the algorithm for smart seed selection. Vladimir’s current research interests include machine learning, image processing, remote sensing, big data and IoT with application in agrifood and biosystems.

Vladimir Crnojević

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

Sanja Brdar

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, PhD is the Assistant Director for Innovation and Business Development. His research focuses on the application of 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 was the leader of BioSense’s team that developed novel Big Data algorithms for yield prediction, seed distribution, and optimisation of sowing strategies, which secured the 1 st prize for BioSense at 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

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

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 Mihajlović obtained a BSc and a MSc degree in Applied mathematics at 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 Faculty of Sciences in Novi Sad. Currently she works as Junior researcher at BioSense Institute with main research interests in the analysis of microbiome data and exploration of the statistical and machine learning techniques in microbiome studies.

Andrea Mihajlović

Aleksandar Antić obtained a BSc in Biomedical Engineering and a MSc degree in Signal Processing at the Faculty of Technical Sciences, University of Novi Sad, in 2018 and 2019. He held scholarships awarded by the Republic of Serbia Young Talents' Fund ''Dositeja'' for the academic years 2017/18 and 2018/19. Since December 2019, he has been working at the BioSense Institute in Novi Sad, as a Junior Research Assistant in the Centre for Information Technologies. He is currently a first-year PhD student of Signal Processing at the Faculty of Technical Sciences, University of Novi Sad. His main research interest is the use of signal processing and machine learning techniques for applications in agriculture, health and environmental protection. Currently his main research focus is working on development of deep learning despeckling models for Sentinel 1 images. He is also actively involved in few Horizon 2020 projects including ANTARES and Cybele.

Aleksandar Antić

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

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This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 825355.               

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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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

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

CYBELE-FOSTERING PRECISION AGRICULTURE AND LIVESTOCK FARMING THROUGH SECURE ACCESS TO LARGE-SCALE HPC-ENABLED VIRTUAL INDUSTRIAL EXPERIMENTATION ENVIRONMENT EMPOWERING SCALABLE BIG DATA ANALYTICS

Centar za inovacije i razvoj poslovanja

Centar za Informacione Tehnologije

Cilj projekta: CYBELE projekat je posvećen kreiranju inovacija i stvaranju vrednosti u poljoprivredno-prehrambenom sektoru primenom metoda precizne poljoprivrede i preciznog stočarstva, kao i podsticanju izgradnje kapaciteta unutar industrijskih i istraživačkih zajednica u pomenutim oblastima.
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O projektu: CYBELE kreira inovacije i stvara vrednost u poljoprivredno-prehrambenom sektoru, kao i u njegovim pod-domenima – preciznoj poljoprivredi i preciznom stočarstvu, što će biti demonstrirano na stvarnim primerima iz industrije, osnažujući time izgradnju kapaciteta unutar industrijske i istraživačke zajednice. S obzirom da poljoprivreda predstavlja ogromnu granu privrede koju odlikuje niska operativna efikasnost, CYBELE nastoji da pokaže kako konvergencija velikih baza podataka, HPC- a, Cloud Computing-a i IoT-a može da napravi revoluciju u poljoprivredi, smanji oskudicu i poveća snabdevanje hranom, donoseći pritom socijalne, ekonomske i ekološke dobrobiti. CYBELE namerava da zaštiti to što su stejkholderi integrisali, neposredan pristup ogromnoj količini različitih tipova podataka, koji dolaze iz različitih izvora, i što imaju sposobnost da stvore vrednost i izvuku podatke, obezbeđujući bezbedan i direktan pristup velikim HPC infrastrukturama, a istovremeno podržavajući otkrivanje podataka, obradu, kombinovanje i usluge vizuelizacije, rešavanje izazova u vidu matematičkih algoritama koji zahtevaju primenu računara velikih performansi (HPC-a). CYBELE razvija opremu koja omogućava testiranje HPC rešenja i isporučuje distribuiranu arhitekturu za upravljanje podacima, kao i strategiju upravljanja podacima, obezbeđujući 1) integrisani, neposredni pristup velikim bazama podataka koje sadrže različite tipova podataka iz mnogobrojnih izvora, 2) virtuelno HPC okruženje koje sadrži podatke i usluge, i koje omogućava izvršavanje eksperimenata sa više parametara iz oblasti poljoprivredno-prehrambene industrije, optimizujući karakteristike obrade velikih baza podataka, i 3) mnoštvo usluga koje mogu biti opšte prirode ili sektorski određene. Povrh svega navedenog, CYBELE obezbeđuje i virtuelnu platformu koja olakšava izvlačenje znanja iz velikih, poljoprivredno-prehrambenih baza podataka, fokusirajući se na podsticanje ideje o donošenju odluka uz pomoć automatizacije, osnaživanje zainteresovanih strana da koriste resurse na ekološki odgovorniji način, poboljšaju svoje odluke koje se tiču nabavke i implementiraju specifična rešenja u lancu ishrane koja se zasnivanju na principima cirkularne ekonomije.

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.

Aleksandar Antić je završio osnovne studije Biomedicinskog inženjerstva i master studije Obrade Signala na Fakultetu Tehničkih Nauka na Univerzitu u Novom Sadu 2018. i 2019. godine. Tokom studija bio je stipendista Fonda za mlade talente Republike Srbije “Dositeja” za 2017/18 i 2018/19 školsku godinu. Trenutno je student prve godine doktorskih studija Obrade Signala na Fakultetu Tehničkih Nauka na Univerzitu u Novom Sadu. Od decembra 2019. godine je zaposlen na Institutu BioSense u Novom Sadu, kao istraživač pripravnik. Njegovo istraživačko interesovanje usmereno je na upotrebu tehnika obrade signala i mašinskog učenja u svrhe poljoprivrede, zdravstvene zaštite i zaštite životne sredine. Fokus trenutnog istraživačkog interesovanja je usmerio na tehnike dubokog učenja za otkljanjanje šuma iz Sentinel 1 slika. Trenutno je aktivni učesnik na nekoliko Horizon 2020 projekata uključujući ANTARES i Cybele.

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.

Isidora Stojačić radi kao menadžer na projektima u Centru za inovacije i razvoj poslovanja Instituta BioSens. Diplomirala je na Univerzitetu u Novom Sadu, na Ekonomskom fakultetu, završila master na Univerzitetu Cattolica del Sacro Cuore, Fakultetu za poljoprivredu kao i na Tehničkom univerzitetu u Minhenu, na Fakultetu prirodnih nauka. Isidora ima iskustva u upravljanju projektima, istraživanju i pisanju predloga projekata. Tokom svoje karijere, imala je dodira sa različitim temama projekata, uglavnom povezanim sa odgovornim istraživanjima i inovacijama (RRI), zajedničkim društvenim stvaranjem vrednosti, socijalnim dizajnom i implementacijom istog, tržišnim i poslovnim strategijama i razvojnim projektima. Bila je uključena u programe Horizon2020, ERASMUS + i srpske nacionalne projekte.

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

Prof. Vladimir Crnojević je direktor Instituta BioSens, istraživačko-razvojnog instituta za informacione tehnologije biosistema, osnovanog sa ciljem da sprovodi osnovna i primenjena multidisciplinarna istraživanja koja će doprineti razvoju održive i efikasnije poljoprivrede i proizvodnje hrane na različitim nivoima: od mikro i nano senzora, preko interneta stvari, agrorobotike, daljinske detekcije, do analitike velikih podataka, mašinskog učenja i veštačke inteligencije.Prof. Crnojević je redovni profesor Univerziteta u Novom Sadu i Associate Professor Extraordinary na Stelenboš Univerzitetu u Južnoafričkoj Republika. Osnovne studije završio je na smeru Energetika, elektronika i telekomunikacije, Fakultet tehničkih nauka Univerziteta u Novom Sadu, gde je odbranio doktorsku disertaciju 2004. godine. Od 2012. do 2015. godine, bio je direktor departmana za Energetiku, elektroniku i telekomunikacije, a 2015. godine osniva Institut BioSense, koji je za veoma kratko vreme postao regionalni lider u EU (FP7 i H2020) projektima, uključujući vodeće projekte kao što su ANTARES , IoF2020 - Internet of Food &amp; Farm i SmartAgriHubs. Od osnivanja, Institut BioSense ostvario je značajnu saradnju sa industrijom i bio je uključen u realizaciju velikog broja akceleratoprskih programa putem kojih je finansirano više od 100 malih i srednjih preduzeća u AgTech domenu. Predvodio je istraživački tim Instituta BioSens koji je 2017. godine osvojio prvo mesto na takmičenju 2017 Syngenta Crop Challenge sa algoritmom za pametan odabir semena. Njegova istraživačka interesovanja uključuju mašinsko učenje, obradu slike, daljinsku detekciju, analitiku velikih podataka i interneta stvari, s primenom u sektoru poljoprivrede i prehrambene industrije.

Dr Sanja Brdar je naučni saradnik i rukovodilac Centra za informacione tehnologije na Institutu BioSense. 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 Pomoćnik direktora za inovacije i razvoj poslovanja. Njegovo istraživanje usmereno je ka primeni naprednih metoda mašinskog učenja i evolutivnih algoritama u poljoprivredi. Treću godinu osnovnih studija je proveo 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.

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.

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 Mihajlović 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 Mlađi istraživač sa temom za istraživanje u oblasti analize mikrobiom podataka i primene statističkih tehnika kao i mašinskog učenja u mikrobiom studijama.

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

   Ovaj projekat je finansiran od strane Evropske Unije, iz programa za Istraživanje i Inovacije-            Horizont 2020, pod sporazumom o grantu broj 825355.


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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<img class="alignnone wp-image-1839 size-large" src="https://api.biosens.rs/wp-content/uploads/2021/06/100-1024x544.jpg" alt="" width="1024" height="544" />

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U praksi se ove napredne kamere koriste za razvoj algoritama za detekciju infekcija na povrću ili otkrivanja modrica na voću i to nekoliko dana pre nego što simptomi postanu vidljivi golim okom. Značajan je i njihov doprinos u praćenju uslova ekosistema i predikciji prinosa, a razvijeni algoritmi pružaju dodatnu informaciju o stanju biljaka, daju procenu roka trajanja poljoprivrednih proizvoda kao, predviđaju prinos i daju objektivne predloge za donošenje odluka nakon žetve.

Hiperspektralne, multispektralne i termalne slike

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

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

Obrada slika sa dronova

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

Obrada satelitskih slika

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

Hab za Sentinel podatke

Analitika velikih podataka se zasniva na obradi heterogenih izvora podataka, različitih rezolucija, koji se prikupljaju u nejednakim vremenskim intervalima. Osim satelitskih podataka, Grupa za veštačku inteligenciju Instituta BioSens prikuplja i koristi podatke dobijene pomoću elektromagnetne sonde koja meri električnu provodnost zemljišta, kao i brojne druge instrumente koji se montiraju na poljoprivredne mašine ili koriste iz ruke. Električna provodnost zemljišta je odličan indikator teksture zemljišta, njene kompaktnosti i hemijskog sastava. Ovi parametri mogu se odrediti na različitim dubinama u zavisnosti od potreba i interesovanja korisnika prema tipu biljaka. Mape elevacije terena nisu ništa manje važne s obziorm na to da određuju pravac i smer oticanja nutrijenata koji je u tesnoj sprezi sa prinosom, dok se ručni uređaji, poput Plant-O-Meter-a razvijenog od strane istraživača Instituta BioSens, uspešno koriste pri monitoringu rasta useva.

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<img class="alignnone wp-image-1487" src="https://api.biosens.rs/wp-content/uploads/2021/06/47-212x300.jpg" alt="" width="220" height="311" /><img class="alignnone wp-image-1490" src="https://api.biosens.rs/wp-content/uploads/2021/06/9-212x300.jpg" alt="" width="220" height="311" />

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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

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

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

CYBELE- Fostering precision agriculture and livestock farming through secure access to large-scale HPC-enabled virtual industrial experimentation environment empowering scalable big data analytics

CYBELE- Fostering precision agriculture and livestock farming through secure access to large-scale HPC-enabled virtual industrial experimentation environment empowering scalable big data analytics

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