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Data Science u poljoprivredi: tehnološka platforma za
automatizaciju daljinske detekcije pomoću bespilotnih letelica
Gostujuće predavanje
Milan Dobrota
18.12.2015
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Globalni izazovi u poljoprivredi ...
2050
Gubici zbog
korova, bolesti i
štetočina su veći
od 20 %
godišnje
9 milijardi
ljudi,
70 % povećanja
proizvodnje
hrane
Većina
poljoprivrednog
zemljišta se već
obrađuje
... proizvesti više hrane sa manje obradivog zemljišta !
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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.
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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
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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
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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.
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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
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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
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Korisnici tehnologije
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Koncepet rešenja
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Video
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Proces, ogledi, analize...
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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
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Dokumentovanje ogleda
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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
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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.
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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
Affinity propagation
damping, sample
preference
Not scalable with
n_samples
Mean-shift
bandwidth
Many clusters, uneven
Not scalable withn_samples cluster size, non-flat
geometry
Distances between points
Spectral clustering
number of clusters
Medium n_samples,
small n_clusters
Graph distance (e.g.
nearest-neighbor graph)
Ward hierarchical clustering number of clusters
Many clusters, uneven
cluster size, non-flat
geometry
Few clusters, even cluster
size, non-flat geometry
Large n_samples andn_clust Many clusters, possibly
ers
connectivity constraints
Distances between points
Graph distance (e.g.
nearest-neighbor graph)
Distances between points
Agglomerative clustering
Many clusters, possibly
number of clusters, linkage Large n_samples andn_clust
connectivity constraints,
type, distance
ers
non Euclidean distances
DBSCAN
neighborhood size
Very large n_samples,
medium n_clusters
Non-flat geometry, uneven
cluster sizes
Distances between nearest
points
Gaussian mixtures
many
Not scalable
Flat geometry, good for
density estimation
Mahalanobis distances to
centers
Birch
branching factor, threshold, Large n_clusters andn_samp Large dataset, outlier
optional global clusterer.
les
removal, data reduction.
Any pairwise distance
Euclidean distance between
points
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Automatic labelling – stress monitoring
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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
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Software
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Poslovni model
Key Partners
Key Activities
Value Propositions
Customer Relationships
Customer Segments
Vendors of equipment
Development of technology
(integration of HW and SW) and
know-how
Delivery of cost-effective
intelligence about crops
Direct contacts and networking
with potential customers
Individual agricultural producers,
using survey services
Increase crops yields and
reduction of risks from pests
Weak relations exists so far, at
the level of pilot projects
Large companies in agriculture
who wish to implement
technology and perform surveys
Vendors of software tools
External consultants (know-how
or sales activities)
Sales activities
Key Resources
Comprehensive technology
integrated with advanced knowhow in use for the benefit of
customer
Skilled experts in area of IT, data Satisfaction of customer needs
science, agriculture,
of improving their farming in
mechatronics...
cost-effective manner
Funding for development and
marketing activities
Government agriculture sectors
Channels
Insurance companies
Raising initial awareness through Large technology companies
internet, fairs and exhibitions
interested in buying technology
Channels of sales is under
Environmental care (decrease of development at this point (direct
sales)
pollution)
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
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Hvala na pažnji!
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