Center for Product Development

Center for Information Technologies

Project aim: Introduction of new technologies and digitization of Serbian agricultural sector, which would allow our country to catch up with the most developed Western countries and achieve better results in terms of profit, savings in agrochemistry, environmental sustainability and food security.
About the project: The idea of the project is to use the drone technology that will provide a better insight into the situation on the ground, based on which decision-making is optimized. After the realization of this project, agricultural extension and professional services and experts will:
1. Have more knowledge in this area
2. Be trained to handle an advanced software solution
3. Be able to give adequate advice to farmers based on drone images
On the other hand, farmers will have the following benefits:
1. Additional source of crop data
2. Better advice from extension services 
3. Savings in agrochemistry
4. Increased yield
5. More effective activity planning
6. Reduced environmental impact (reduced land quality degradation)
In this way, agriculture in Serbia accepts the possibilities of the 4.0 revolution and begins the transition to digital form.

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

Mina Bjelica works as a junior associate in the product development center at the BioSense Institute. She graduated in 2019 at the University of Novi Sad, at the Faculty of Science on Information Technology department. At BioSense, she is currently working on the development and maintenance of several web applications in the programming language for backend – Java (EJB and Spring framework), for frontend: TypeScript (Angular framework), AngularJS framework, HTML, CSS, languages for datebase: SQL and HQL, and servers: wildfly and tomcat.

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Mina Bjelica

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

Milijana Vujasinović

Milica Milovanović works as junior associate at BioSense Institute since October 2019. She received a bachelor's degree from the Faculty of Sciences, University of Novi Sad, Serbia in 2019 in the area of Information technologies. Currently, her main responsibility within the BioSense Institute is developing web applications. She is developing backend in Java and Python frameworks (Spring, EJB and Django). For frontend part of applications she is working with Angular and AngularJS. She also works with databases and servers (Wildfly and Tomcat).

Milica Milovanović

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

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

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

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

uprava-za-agrarna-placanja

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

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

Geospatial data analytics

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

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

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

Deep Learning

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

Analysis of collected data using unmanned aerial vehicles

Centar za razvoj proizvoda

Centar za Informacione Tehnologije

Cilj projekta: Uvođenje novih tehnologija i digitalizacija poljoprivrede Srbije, čime bi naša zemlja uhvatila korak sa najrazvijenijim Zapadnim zemljama i ostvarivala bolje rezultate sa stanovišta profita, ušteda u agrohemiji, ekološke održivosti i prehrambene sigurnosti.
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Opis projekta: Ideja je da se do cilja dođe upotrebom tehnologije dronova koji omogućuju bolji uvid u stanje na terenu na osnovu kog se optimizuje donošenje odluka. Nakon realizacije ovog projekta, poljoprivredne savetovdavne i stručne službe će:
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<ol>
<li>Posedovati više znanja u ovoj oblasti</li>
<li>Biti obučeni za rukovanje naprednim softverskim rešenjem</li>
<li>Biti u mogućnosti da daju adekvatne savete poljoprivrednicima na osnovu slika sa drona</li>
</ol>
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Sa druge strane, poljoprivrednici će imati sledeće benefite:
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<ol>
<li>Dodatan izvor podataka o usevima</li>
<li>Kvalitetniji saveti poljoprivrednih stručnih službi</li>
<li>Uštede u agrohemiji</li>
<li>Povećan prinos</li>
<li>Efektivnije planiranje aktivnosti</li>
<li>Smanjeni uticaj na životnu sredinu (smanjena degradacija kvaliteta zemljišta)</li>
</ol>
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Na ovaj način poljoprivreda Srbija prihvata mogućnosti revolucije 4.0 i započinje punu tranziciju ka digitalnom obliku.

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.

Mina Bjelica radi kao mlađi saradnik u centru za razvoj proizvoda na BioSense Institutu. Diplomirala je 2019. godine na Univerzitetu u Novom Sadu, na Prirodno – matematičkom fakultetu, na smeru Informacione tehnologije. U BioSensu trenutno radi na razvoju i održavanju nekoliko web aplikacija u programskom jeziku Java (EJB I Spring okruženja) za backend, za frontend koristi jezike: TypeScript (Angular okruženje), AngularJS okruženje, HTML, CSS, jezike koje koristi za baze podataka su: SQL i HQL, i servere: wildfly i tomcat.

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Milijana Vujasinović je rukovodilac Centra za razvoj proizvoda. Ima višegodišnje iskustvo u razvoju i implementaciju softverskih rešenje. U razvoju softverskih rešenja učestvovala je u početku kao programer i biznis analitičar, a kasnije i kao projektant rešenja i menadžer proizvoda. Bogato iskustvo je stekla radeći kao ERP konsultant i menadžer projekata u implementacijama softverskih rešenja u različitim privrednim granama – poljoprivrednoj,veleprodaji i maloprodaji, prehrambenoj industriju, tekstilnoj industriju, metalurgiji, industriji alata i kablova, građevinskoj industriju, uzlužnoj delatnost kao i u javnim preduzećima. Sertifikovani je SAP FI i SAP Project Manager. Više od 3 godine je upravljala PMO (Project Managment Office), odeljenjem koji kontroliše i unapređuje proces upravljanje projektima.

Milica Milovanovic radi kao mlađi saradnik u BioSense Institutu. Diplomirala je 2019. godine Univerzitetu u Novom Sadu na Prirodno-Matematičkom fakultetu, na smeru Informacione tehnologije. Na institutu trenutno radi na razvijanju web aplikacija. Backend implementira u Java i Python okruženjima (Spring, EJB i Django). Za frontend koristi Angular i AngularJS. Takođe radi sa bazama podataka i serverima (Wildfly i Tomcat).

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 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.

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.

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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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

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-1486 " src="https://api.biosens.rs/wp-content/uploads/2021/06/46.jpg" alt="" width="220" height="312" /><img class="alignnone wp-image-2435" src="https://api.biosens.rs/wp-content/uploads/2021/06/prostorna-212x300.jpg" alt="" width="220" height="311" />

<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

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

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

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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Analysis of collected data using unmanned aerial vehicles

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CRP

CIT