Data Science u poljoprivredi: tehnološka platforma za automatizaciju daljinske detekcije pomoću bespilotnih letelica

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1 Data Science u poljoprivredi: tehnološka platforma za automatizaciju daljinske detekcije pomoću bespilotnih letelica Gostujuće predavanje Milan Dobrota

2 2 Globalni izazovi u poljoprivredi milijardi ljudi, 70 % povećanja proizvodnje hrane Gubici zbog korova, bolesti i štetočina su veći od 20 % godišnje Većina poljoprivrednog zemljišta se već obrađuje... proizvesti više hrane sa manje obradivog zemljišta!

3 3 Uvod Precizna poljoprivreda (Precision Agriculture) is the application of geospatial techniques and sensors (e.g. GIS, Remote sensing, GPS) to identify variations in the field and to deal with them. High-resolution satellite imagery was more commonly used. Small unmanned aerial systems (UAS), are shown to be a potential alternative given. Daljinska detekcija (Remote Sensing) predstavlja metod prikupljanja informacija putem sistema koji nisu u direktnom, fizičkom kontaktu sa ispitivanim objektom. U užem smislu obuhvata analizu i interpretaciju različitih snimaka zemljišta. Informacije se prikupljaju registrovanjem i snimanjem odbijene ili emitovane energije objekta i obradom, analiziranjem i korišćenjem tih podatka. Bespilotne letelice (UAV, UAS) platforms offer new possibilities to agriculture in order to obtain high spatial resolution imagery delivered in near-real time. The increase of spatial and temporal resolution of the geomatic products obtained with UAVs should be accompanied with the use of new algorithms and techniques for information abstraction from these products. A clear example of this fact is the use of vegetation indices such as NDVI, which can be substituted by computer vision techniques or other indices based on RGB bands information, which can be obtained with inexpensive sensors.

4 4 Šta radimo Razvioj tehnološke platforme za daljinsku detekciju AgriSens Tehnologija je deo Precizne poljoprivrede koja koristi: Daljinsku detekciju za snimanje velikih obradivih površina Prikupljanje velike količine podataka u realnom vremenu Obradu i analizu podataka koristeći statističke metode i algoritme sposobne da uče Izlazi iz sistema su geo-referencirane mape područja sa procesiranim i analiziranim podacima od interesa za specifični zahtev u poljoprivredi

5 5 Kako to radimo Snimanje i obrada slika visoke rezolucije i odgovarajućeg spektra (RGB, NIR...), snimljene uz pomoć bespilotnih letelica i pre-procesiranje algoritmima obrade slika. Obrada podataka izvlačenjem skrivenih šablona u podacima dobijenim iz slike, koristeći analitičke algoritme, što takođe uključuje samoučeće algoritme, odlučivanje pomoću neuronskih mreža i sl. Analiza korišćenjem Vegetativnih Indeksa (VI) dobijenih obradom podataka rezultat će biti geo-referencirana mapa posmatranog polja. Algoritmi će u prvim iteracijama ulazne parametre dobijati od eksperata, da bi kasnije sami učili i ispravljali se

6 6 Inovacija UAV (bespilotne letelice) uz niže troškove omogućavaju češća snimanja, visoku rezoluciju zahvaljujućim niskim visinama i malim brzinama leta i značajno manje potrebne obuke za korišćenje. Satelitski snimci i fotografije iz vazduha su ograničeni vremenom potrebnim za snimanje, niskom rezolucijom, zavisnošću od oblaka i visokim troškovima za ažurne slike. Integrisanost i sveobuhvatnost alata za prikupljanje i obradu velike količine podataka, omogućava krajnjim korisnicima gotove rezultate, snimanjem u različitim spektrima, data mining-om, mašinskim učenjem i automatizacijom čitavog procesa, bez potrebe za ekpertskim znanjem korisnika. Očuvanje životne sredine primenom SSWC (site specific weed control) principa ima značajne ekološke prednosti smanjenom upotrebom pesticida i hebicida.

7 7 Primena tehnologije u obuhvatu projekta Identifikacija korova, u prvoj fazi kod široko-rednih zasada (kukuruz, suncokret, šećerna repa, itd.) Detekcija stresa koji može biti posledica oboljenja, štetočina ili suše, praćenjem promena na listovima useva. Razlike refleksije među različitim delovima EM spektra se koriste za razlikovanje zdrave vegetacije od uvenule ili bolesne. Brojanje biljaka i procena prinosa, naročito kod široko-rednih i višegodišnjih zasada

8 8 Drugi primeri primena Detekcija hlorofila: EM energija emitovana od useva varira tokom cele sezone i tokom dana u zavisnosti od sunčevog zračenja. Detekcija nedostatka azota: distributeri azotnog đubrivo nemaju algoritam po kome upravljaju količinom đubriva distribuiranom na pojedinim delovima zemljišta što može dovesti do povećanja troškova i smanjenja prinosa. Klasifikacija zemljišta: fizičke osobine zemljišta su u korelacijama sa reflektovanim elektromagnetnim talasima određenih talasnih dužina i zbog toga slike imaju potencijal u automatskoj klasifikaciji vrsta zemljišta i njihovom mapiranju

9 Korisnici tehnologije 9

10 Koncepet rešenja 10

11 Video 11

12 Proces, ogledi, analize... 12

13 13 Prikupljanje slika Prikupljanje slika može biti podeljeno u tri faze: Planiranje misije Letenje UAV-om i slikanje (RGB & NDVI, Normalized Difference Vegetation Index) Spajanje-mozaiking orthophoto slika

14 Dokumentovanje ogleda 14

15 15 Ekstrakcija vegetacijskih indeksa Vegetation interacts with solar radiation in a different way than other natural materials. The vegetation spectrum (figure 3) typically absorbs in the red and blue wavelengths, reflects in the green wavelength, strongly reflects in the near infrared (NIR) wavelength, and displays strong absorption features in wavelengths where atmospheric water is present. Different plant materials, water content, pigment, carbon content, nitrogen content, and other properties cause further variation across the spectrum

16 16 Automatic labelling Provides the automatic proposal of the OOI on the image. Various clustering methods are be used for this task, namely k-means and its modifications, DBSCAN and its modifications, OPTICS and its modifications.

17 17 Automatic labelling Method name Parameters Scalability Usecase Geometry (metric used) K-Means number of clusters Very large n_samples, General-purpose, even medium n_clusterswith Mini cluster size, flat geometry, Batch code not too many clusters Distances between points Affinity propagation damping, sample preference Not scalable with n_samples Mean-shift bandwidth Not scalable withn_samples Many clusters, uneven cluster size, non-flat geometry Many clusters, uneven cluster size, non-flat geometry Graph distance (e.g. nearest-neighbor graph) Distances between points Spectral clustering number of clusters Medium n_samples, small n_clusters Few clusters, even cluster size, non-flat geometry Graph distance (e.g. nearest-neighbor graph) Ward hierarchical clustering number of clusters Large n_samples andn_clust ers Many clusters, possibly connectivity constraints Distances between points Agglomerative clustering number of clusters, linkage type, distance Large n_samples andn_clust ers Many clusters, possibly connectivity constraints, non Euclidean distances Any pairwise distance DBSCAN neighborhood size Very large n_samples, medium n_clusters Gaussian mixtures many Not scalable Non-flat geometry, uneven cluster sizes Flat geometry, good for density estimation Distances between nearest points Mahalanobis distances to centers Birch branching factor, threshold, optional global clusterer. Large n_clusters andn_samp les Large dataset, outlier removal, data reduction. Euclidean distance between points

18 Automatic labelling stress monitoring 18

19 19 Image Recognition Counting: Template matching, a is a technique in digital image processing for finding small parts of an image which match a template image Haar-like feature based cascade sums up the pixel intensities in each region and calculates the difference between these sums. This difference is then used to categorize subsections of an images are digital image features used in object recognition. Stress identification and Yield estimation: Histogram matching is the transformation of an image so that its histogram matches a specified histogram. An image histogram is a type of histogram that acts as a graphical representation of the tonal distribution in a digital image

20 Software 20

21 Poslovni model 21 Key Partners Key Activities Value Propositions Customer Relationships Customer Segments Vendors of equipment Vendors of software tools External consultants (know-how or sales activities) Development of technology (integration of HW and SW) and know-how Sales activities Key Resources Delivery of cost-effective intelligence about crops Increase crops yields and reduction of risks from pests Comprehensive technology integrated with advanced knowhow in use for the benefit of customer Direct contacts and networking with potential customers Weak relations exists so far, at the level of pilot projects Channels Individual agricultural producers, using survey services Large companies in agriculture who wish to implement technology and perform surveys Government agriculture sectors Insurance companies Skilled experts in area of IT, data science, agriculture, mechatronics... Funding for development and marketing activities Satisfaction of customer needs of improving their farming in cost-effective manner Environmental care (decrease of pollution) Raising initial awareness through internet, fairs and exhibitions Channels of sales is under development at this point (direct sales) Large technology companies interested in buying technology Cost Structure Revenue Streams Costs for developers and engineers to develop technology Fees for external consultants (not part of the core project) Purchase of hardware equipment (UAV, camera, etc.) Expenses related to sales activities Expenses related to field research Services of crops examinations provided as a service Sold out technology, either fully or partially It is expected that customers currently pay more expensive technology Technology selling would be less frequent but generating bigger revenue at one shot

22 22 Hvala na pažnji!