Center for Product Development

Center for Biosystems

Center for Innovation and Business Development

Project aim: eLTER PLUS project that will address biodiversity loss, biogeochemical controls of ecosystem functions, the climate-water-food nexus, and socio-ecological systems. By engaging current and new users and developing the operations of cross- and transdisciplinary research, as well as strengthening community and service building, the project will expand on the research capacities.
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About the project: Long-term ecosystem research is essential to improve our knowledge of the structure and functions of ecosystems and their long-term responses to environmental, societal, and economic drivers. Networking, joint research activities, and transnational, remote and virtual access are the three main pillars of the EU-funded eLTER PLUS. The European long-term ecosystem, critical zone, and socio-ecological systems in research infrastructure (eLTER RI) have been assessed by the European Strategic Forum for Research Infrastructures (ESFRI) as a great potential for closing this gap in the European landscape. The Whole System approach will extract relevant scientific and policy-relevant information through co-designed, transdisciplinary research in collaboration with a variety of stakeholders at local, regional and EU level. Coordinated actions also focus on collaborating with peer research institutions to maximize synergies, increase efficiencies and catalyze a holistic understanding of ecosystem function, as well as the development of virtual laboratories in which on-site data are linked to other data sources, e.g., Copernicus.

Vladan Minić

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.

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

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

eLTER Plus

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This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 871128.      


Horizon 2020

BioSense participates in a range of pan-European initiatives that aim to centralise environmental data coming from various regions, sources and institutions. Such a database would provide researchers, government institutions and NGOs precious information about the state of ecosystems around Europe along with an insight on how they are changing in time.

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Environmental and ecological data is very heterogeneous. It encompasses logs from sensors, expert observations, photos from smartphones and UAVs, lab analyses and others. Individual data from one source or one region provides little information, as it cannot be analysed in a wider context. Also, data is usually kept at individual institutions separately, which poses further hurdles for comprehensive analysis and directly contradicts the EU principles of open science.

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For these reasons, we participate in a suite of European projects where we are developing a platform for deposition of environmental and ecological data. The platforms facilitates networking between European institutions and a collaborative approach to tackling the problems of climate change, global warming, habitat loss and altered competitive relationships within flora and fauna.

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Ecology Platforms

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

eLTER PLUS-European long-term ecosystem, critical zone and research infrastructure of socio-ecological systems PLUS

Centar za razvoj proizvoda

Centar za Biosisteme

Centar za inovacije i razvoj poslovanja

Cilj projekta: eLTER PLUS bavi se gubitkom biodiverziteta, biogeohemijskim kontrolama funkcija ekosistema, klimom/vodom/hranom i socijalno-ekološkim sistemima. Angažovanjem velikog broja učesnika kroz transdisciplinarno istraživanje i jačanjem zajednice, cilj projekta je da proširi istraživačke kapacitete.
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O projektu: Dugoročno istraživanje ekosistema je od suštinskog značaja za poboljšanje našeg znanja o strukturi i funkcijama ekosistema i njihovim dugoročnim odgovorima na ekološke, društvene i ekonomske pokretače. Umrežavanje, zajedničke istraživačke aktivnosti i transnacionalni, daljinski i virtuelni pristup tri su glavna stuba projekta eLTER PLUS. Evropski dugoročni ekosistem, kritična zona i socio-ekološki sistemi u istraživačkoj infrastrukturi (eLTER RI) ocenjeni su od strane Evropskog strateškog foruma za istraživačke infrastrukture (ESFRI) kao veliki potencijal za zatvaranje ovog jaza u evropskom pejzažnom predelu. The Whole System pristup izvući će značajne naučne informacije i informacije relevantne za politiku kroz ko-dizajnirana, transdisciplinarna istraživanja u saradnji sa različitim zainteresovanim stranama na lokalnom, regionalnom i evropskom nivou. Usklađene akcije takođe su usredsređene na saradnju sa istraživačkim institucijama kako bi se maksimalizovale sinergije, povećale efikasnosti i katalizovalo holističko razumevanje funkcije ekosistema, kao i na razvoj virtuelnih laboratorija u kojima su podaci na licu mesta povezani sa drugim izvorima podataka, npr. Kopernikus.
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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.

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

 Ovaj projekat je finansiran od strane Evropske Unije, iz programa za Istraživanje i Inovacije-
 Horizont 2020, pod sporazumom o grantu broj 871128. 

Horizont 2020

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

Obrada slika sa dronova

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

Obrada satelitskih slika

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

Hab za Sentinel podatke

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

Analitika prostornih podataka

BioSens učestvuje u raznim panevropskim inicijativama kojima je cilj da centralizuju podatke o životnoj sredini koji dolaze iz različitih regiona, izvora i institucija. Takva baza podataka pružila bi istraživačima, vladinim institucijama i nevladinim organizacijama dragocene informacije o stanju ekosistema širom Evrope, kao i uvid u to kako se oni vremenom menjaju.

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Ekološki podaci su vrlo heterogeni. Obuhvataju zapise sa senzora, stručna zapažanja, fotografije sa pametnih telefona i dronova, laboratorijske analize i druge. Pojedinačni podaci iz jednog izvora ili jednog regiona pružaju malo informacija, jer se ne mogu analizirati u širem kontekstu. Takođe, podaci se obično čuvaju u pojedinačnim institucijama odvojeno, što predstavlja dodatne prepreke za sveobuhvatnu analizu i direktno je u suprotnosti sa principima otvorene nauke EU.

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Iz tih razloga učestvujemo u nizu evropskih projekata gde razvijamo platformu za čuvanje podataka o životnoj sredini i ekologiji. Platforme olakšavaju umrežavanje između evropskih institucija i zajednički pristup rešavanju problema klimatskih promena, globalnog zagrevanja, gubitka staništa i izmenjenih odnosa unutar flore i faune.

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Ekološke platforme

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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eLTER PLUS- European long-term ecosystem, critical zone, and research infrastructure of socio-ecological systems PLUS

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