precise weed management is very useful under large land holdings which reduces cost of cultivation to a greater extent. remote sensing plays a major role in site specific weed management

1. Professor Jayashankar Telangana State Agricultural University
College of Agriculture, Rajendranagar, Hyderabad
AGRON 503
SUBMITTED BY
M. VEERENDRA
RAM/18-07
DEPARTMENT OF AGRONOMY
Submitted to
Dr.M.Madhavi
Principle Scientist & Head
AICRP- Weed Management

2. WEED MANAGEMENT USING REMOTE SENSING

3. Introduction
• The current weed management practise includes
spraying the whole agricultural field with chemical
herbicides.
• Although this seems to be effective, it has huge effect
on the surrounding environment due to excessive use
of chemicals.
• As weeds don’t spread over the entire field it is waste
to spray herbicide to the entire field.
• Precision agriculture is a farming management concept
based on observing measuring and responding to the
variability in the crops.

4. Precision agriculture/ Satellite farming/Site specific
crop management is the application of technologies
and principles to manage spatial and temporal
variability associated with all the aspects of
agriculture to improve the productivity.

5. Remote sensing - sensing things from a distance.
• Art and science of obtaining useful information about
an object with out being in physical contact with it
without physically contact between the object and sensor.
• Remote sensing uses the electromagnetic spectrum to
image the land, ocean and atmosphere.
All objects on the surface of the earth have their own
characteristic spectral signature

6. Figure 1. The process of Remote Sensing

7. Space based platforms

8. Unmanned aerial vehicles
Air based platforms

9. Unmanned ground vehicles
Ground based platforms

10. Recently, applications of remote sensing using
UAVs have shown great promise in precision
agriculture as they can be equipped with various
imaging sensors to collect high spatial, spectral,
and temporal resolution imagery

11. Spatial resolution

12. Spectral resolution

13. Figure 2. reflectance and absorbance of different wavelengths by different components

14. Figure 3. spectral reflectance of different weeds
Mustafa et al., 2013
Grass lands of Texas, USA1)

15. Figure 4.The difference in reflectance in a weedy plot and weed free plot is
due to more green vegetation covering the soil. Chang et al., 2009
2)

16. One of the important and challenging components of
SSWM is weed recognition and field mapping for an
appropriate early automatic weed control
(Shaner and Beckie,(2014).
However, the reflectance characteristics of crops and
weeds are generally similar in their early growth stages,
thus imposing additional difficulties to discriminate
between them (López-Granados,2011; Perez-Ortiz et al.,
2016).
Moreover, weeds can grow in small patches in the early
season, which also adds challenges and requires high
resolution imagery to detect them.

18. Figure 5.Example of the spatial and spectral combination results using an SVM classifier.
(a)Multispectral orthoimage;
(b) Crop (green) and weed (red) location deduced from spatial information;
(c) Weed (green) and crop (red) location deduced from spectral information;
(d) Weed(green) and crop (red) location deduced from the combination of spatial and
spectral information.
Marine et al., 2018
France, Maize crop having weeds
like Chenopodium album, Cirsium arvense3)

19. A study was conducted at the experimental
fields of the Institute for Agricultural and
Fisheries Research (ILVO) in the agricultural
region of Merelbeke, which is located in East-
Flanders Province, Belgium. The area of the
maize plot was about 150m2. The naturally
infested weed species in the maize plot included
Convolvulus arvensis, Chenopodium album and
Digitaria sanguinalis species
Junfeng et al.,2017
4)

20. Figure 6.Partial view of image processing steps for inter-row weed detection.
(a) raw RGB image, (b) vegetation index of ExG, the dark pixel represents background
and the light pixel represents vegetation, (c) binarized image via Otsu’s algorithm, (d)
pre-processed binary image, (e) Canny binary edge image, (f) detecting maize row
line by the Hough algorithm, (g)masking maize crop, (h) inter-row weed binary image

21. Figure 7. Intra-row weed
classification result from
the OBIA.
(a) The classification results
by the RF
(b) large view from marked
yellow box area.
• Concerning UAV-based remote sensing, Object-based Image Analysis
(OBIA) is a common methodology in classifying objects. The OBIA first
identifies spectrally and spatially homogenous objects according to its
segmentation results and then it combines spectral, textural and
geometry information from objects to boost classification results

22. OBIA

23. Figure 8.Procedure steps of fusion of intra- and inter row weed. (a) the
partial view of intra-row weed binary image, (b) the partial view of inter-
row weed binary image, (c) the partial view of inter and intra row weed
fusion result, (d) the detected weeds marked with the red bounding box.
Junfeng et al.,2018

24. OBIA
Pixel based
Figure 9.Procedure to distinguish crop and weeds( inter row and intra row)

25. 11(a) The OBIA weed predictions for the 20 selected
windows, (b) the relationship between the OBIA
predictions and the GT of weed densities.
10(a) The Hough weed predictions for the 20 selected
windows, (b) the relationship between the Hough predictions
and the GT of weed densities

26. 12(a) The weed predictions from the fusion of the Hough features and OBIA for the 20
selected windows, (b) the relationship between the values from the proposed method
and the GT of weed densities.
Junfeng et al.,2017

27. • Sugarcane is a long duration crop which reaches its maturity in
11–12 months. Crop growth is very slow at the initial stage i.e. it
takes 25–30 days to complete germination and another 90–95
days to complete tillering
• Weeds in these sugarcane fields are classified as grasses, sedges,
broad leaved weeds and climbers wherein Cynodon dactylon,
Panicum species, Sorghum halopense, Chloris barbata,
Dactyloctenium aegyptium are family of grasses, Cyperus iria
and Cyperus rotundus are family of sedges, Trianthema
portulacastrum, Amaranthus viridis, Portulaca oleraceae,
Commelina bengalensis, Cleome viscosa and Chenapodium
album are broad leaved weeds and Convolvulus arvensis,
Ipomea sepiaria and Ipomea alba are climbers
5)

28. • The weed detection system has four major steps. They are:
• (i) Colour based greenness identification
• (ii) Texture extraction
• (iii) Feature vector generation
• (iv) Classification.
• The system also considers the surface texture of the leaf parts
(venation) rather than the size and shape of the individual leaf
Fuzzy real time classifier

29. Figure 13.Data set regarding the crop

30. Leaf-texture and venation
extraction from
10 species
• a.Ipomea alba
• b.Convolvulus arvinse
• c.Coccinia grandis
• d.Trianthema portulacastrum
• e.Sugarcane
• f.Amaranthus viridis
• g.Cyanotis axillaris
• h.Physalis minima
• i.Comalina bengalensis
• j.Cyperus rotundus.
Figure 14.Data set regarding the weeds

31. Colour based
navigation
process
weed detecting vehicle

32. Figure 15.Architecture of the weeding robotic model.

33. A field robotic model in which the weed detection algorithm is implemented has
been tested in different sugarcane fields and it gives an overall accuracy of 92.9%
and processing time of 0.02 s.
Sujaritha et al., 2017

34. • Monitoring high density of plants (mache salad) with
the undesired presence of weeds is challenging since
the intensity or color contrast between weed and crop
is very weak.
• Therefore, this problem is well adapted to test scatter
transform which is a texture-based technique.
• A scattering transform builds a stable informative
signal represents for classification. It is effective in
image, audio and texture discrimination.
France, Mache salads
Pejman et al., 2019
6)

35. Figure 16. view of the imaging system fixed on a robot
moving above mache salads of high density with weeds

36. RGB images from top view for the detection of weed out of plant
used as testing data-set

37. Simulation pipeline for the
creation of images of plant with
weed
Figure 17.Illustration of different types of
weeds used for the experiment

38. Figure 18.Anatomical scales where (Wi,Pi) presents the scales of weeds and
plants respectively; (W1, P1) points toward the texture of the limb, (W2, P2)
indicates the typical size of leaflet and (W3, P3) stands for the width of the
veins. Sw and Sp show the size of a leaf of weed and plant, respectively. The
classification of weed and plant is done at the scale of a patch taken as 2
max(Sp, Sw) in agreement with a Shannon-like criteria

39. Figure 19. Output images for each class (weed on left and plant on
right) and for each layer m of the scatter transform.
In the application of scatter transform to classification found in the
literature so far, the optimization of the architecture was done.

40. 20(a) (97.27%) accuracy 20 (b) (69.45%)accuracy
Visual comparison of the best and the worst recognition of weeds
and plants by scatter transform.
Pejman et al., 2019

41. Figure 21.Visualization on Drop-on-Demand herbicide application
Norway, Carrot field having weeds like Chenopodium album, Poa annua, Stellaria media
Utstumo., 2019
7)

42. The 2017 Asterix robot prototype in field trials in Central Norway.

43. Figure 22.Two uppermost images: same plot (plot No. 1001) before and
17 days after glyphosate application with the robot.
Two bottom images: untreated plot (plot No. 1002)

44. • The label application for Glyfonova Plus ranges from
540 g/ha to 2880 g/ha depending on the types of
weeds and weed pressure (Cheminova AS, 2015).
• A treatment scheme with the robot and the DoD
system, would consist of 2-3 treatments in combination
with mechanical weed control in between the rows.
Building on the experience from the lab and field trials,
we would estimate a total application of 50 - 150 g/ha
glyphosate. This represent a ten-fold reduction in
applied herbicide.
Utstumo., 2019

45. References:
.
Dwivedi, A., Naresh, R.K., Kumar, R., Yadav, R.S. and Rakesh,K.,2017:Precision agriculture.
Gaoa, J., Liaob,W., Nuyttensc,D., Lootensd,P., Vangeytec,J., Pižuricab,A., Hee,Y. and Jan G.
Pietersa,2017:Fusionofpixelandobject-based featuresforweedmappingusingunmanned aerial vehicle
imagery. International Journal of Applied Earth Observation and Geoinformation, 67: 43-53
Hassanein,M. and El-Sheimy,N. An efficient weed detection procedure using UAV imagery system
for precision agricultural applications. Remote Sensing and Spatial Information Science.
Ishaq ,W.I.W., Hudzar,R.M. anRidzuan,R.M.N. 2011:Development of variable rate sprayer for oil
palm plantation. Bulletin of polish academy of sciences ,59:3
Lottes,P., Behley,N., Milioto,A. and Stachniss, C. 2018:Fully Convolutional Networks with
Sequential Information for Robust Crop and Weed Detection in Precision Farming. IEEE Robotics and
Automation Letters
Peña,J.M., Sánchez,J.T. and Pérez, A.S, 2015: Quantifying Efficacy and Limits of Unmanned Aerial
Vehicle (UAV) Technology for Weed Seedling Detection as Affected by Sensor Resolution. Sensors ,
15: 5609-5626
Rasti ,P., Ahmad, A., Samiei ,S., Belin,E. and Rousseau,D.2019:Supervised Image Classification by
Scattering Transform with Application to Weed Detection in Culture Crops of High Density. Remote
Sensing, 11: 249
Tamouridou ,A.A., Alexandridis, T.K. , Pantazi ,X.E. , Lagopodi,A.L., Kashefi ,J., Kasampalis ,D.,
Kontouris ,G and Moshou,M.2017:Application of Multilayer Perceptron with Automatic Relevance
Determination on Weed Mapping Using UAV Multispectral Imagery. Sensors , 17: 2307
Utstumoa,T., Urdala,F.,Brevika,A.,Døruma,J.,Netlandc,J. and Overskeida. 2019:Robotic in-row weed
control in vegetables, Computers and Electronics in Agriculture
Xu,Y., Gao,Z., Khot,L., Meng,X and Zhang ,Q .2018:A Real-Time Weed Mapping and Precision
Herbicide Spraying System for Row Crops.Sensors,18: 4245
Zisi ,T., Alexandridis ,T.K., Kaplanis,S., Navrozidis ,L., Tamouridou,A.A., Lagopodi ,A., Moshou,D. and
Polychronos,V. 2018: Incorporating Surface Elevation Information in UAV Multispectral Images for
Mapping Weed Patches.Journal imaging, 4: 132