Remote sensing and GIS techniques can provide timely and accurate information for agricultural monitoring over large areas. Remote sensing uses sensors aboard satellites or drones to capture electromagnetic radiation reflected or emitted from crops and soil. GIS allows integration of spatial data for analysis. Applications include crop identification and acreage estimation, growth monitoring, soil moisture and fertility assessment, pest and disease detection, and yield estimation. Various sensors such as multispectral, thermal, LIDAR and hyperspectral are used to analyze vegetation, soil properties, and assess crop health. Drones equipped with different sensors can assist with crop scouting, inventory management and precision agriculture.

1. Remote Sensing Geographic Information System

2. DEPARTMENT OF SOIL SCIENCE AND
AGRICULTURAL CHEMISTRY,
COLLEGE OF AGRICULTURE, LATUR
Master Seminar
On
“Advances on Remote Sensing, GIS and Drone Technique in
Agriculture”
Presented to Seminar Incharge
Dr. V. G. TAKANKHAR
Associate Professor,
Dept. of Soil Science and Agril. Chemistry,
College of Agriculture, Latur
Presented by
SHRIVASTAV AKSHAY SHITALKUMAR
Reg. No. 2018A/46ML

3. Introduction
• Remote sensing technique widely used in agriculture.
• Use of remote sensing is necessary as monitoring of agricultural activities
faces special problems not common to other economic sectors.
• Production depends on the physical landscape (e.g., Soil type), as well as
climatic driving variables and agricultural management practices.
• All variable are highly variable in space and time. Moreover, as productivity
can change within short time periods, due to unfavorable growing conditions,
agricultural monitoring systems need to be timely.
• Remote sensing with help of GIS can significantly contribute to providing
timely and accurate picture of the agriculture sector, as it is very suitable for
gathering information over large areas with high revisit frequency.
• These technologies have many applications such as crop identification and
crop acreage estimation, crop growth monitoring, soil moisture estimation,
soil fertility evaluation, crop stress detection, detection of disease and pest
infestation, drought and flood condition monitoring, yield estimation,
weather forecasting, mapping and monitoring natural resources.
FAO, 2011 and Shanmugapriya et al., (2010)

4. Image courtesy : Google Images
What is Remote Sensing and GIS?
Remote Sensing: Methodology
Remote Sensing: Methodology
EARTH STATION
ATMOSPHERE
IRS SATELLITE
PATH RADIANCE
WHEAT FIELD
INDIA
INDIA
INDIA
INDIA
SUN
Capture
Storage
Manipulate
Visualization

5. Electromagnetic spectrum
Invisible radiation  Detected by RS instrument and used for our advantage
IR (0.7 um – 100 um)
- Reflected IR (0.7 um – 3.0 um)
- Em/ther IR (3.0 um – 100 um)
Image Courtesy: Google

6. Spectral reflectance characteristics of leaf
Swain et al., (1978)
Green curve : Leaf
Pink curve : Soil
Blue curve : Water

7. Relation between plant pigment and spectral reflectance of maize
VNMKV, Parbhani Zagade (2011)
Total chlorophyll concentration
established linear relationship
with RVI, NDVI and SAVI

8. Crop identification
The spectral profiles of various Agricultural crops
Uttarakhand, MP, UP and Maharashtra Navalgund et al., (1991)
• Understanding of the physical and physiological properties of plant and their
interaction with the incident radiation is the key element in crop identification
through remote sensing. (Sai et al. 2004)

9. Crop identification with high resolution
GPBU&T, Pantnagar
Identification of representative sites of various crops and their heterogeneity on image based
on ground truth
Uttarakhand, MP, UP and Maharashtra Navalgund et al., (1991)

10. Crop acreage estimation
Sesha Sai, M.V.R., Ramana, K.V., and Hebbar R., (2004) Remote sensing application. NRSC (1) : 1-19.
Digitally Classified Image
NRSC, Hydrabad Sai et al., (2004)

11. Growth Profile for Identification
Land use/Land cover mapping
Ausa, Latur Sayambar (2015)
Fig. Land use/Land cover map of Tungi watershed
Table. Land use/Land cover classes of Tungi
watershed
92.89 % area of watershed is under cultivation

12. Growth Profile for Identification
Varietal discrimination
NRSC, Hydrabad Thenkabail (2002)
• Thenkabail (2002) had found that to discriminate between crops
(wheat, barley, chick pea, cumin lentil and vetch) four most optimum
hyperspectral bands are 547, 675, 718 and 904 nm and in discriminating
between sugercane varieties 550, 680 and 800 nm useful.

13. Silt loam reflectance spectra for various moisture contents
Moisture estimation in soil
SAC, Ahmadabad Tripathy (2018)
• Inverse relationship exist between soil moisture content and spectral
reflectance.
• OH bond

14. Narrow water absorption band
Narrow absorption band
Paris basin Kruse et al. (1991)
• Absence of bound water in
Kaolinite shows a weak band
at 1.9 um.
• Montmorillonite shows
very strong bands at 1.9 um
as well as 1.4um.
• Clay minerals have major
influence on the VNIR
portion of spectral
reflectance.

15. Pest and disease infestation
Forest Research Institute, Dehradun Acharya et al., (2015)
• Green peak at circa 550 nm
• Indices of diseased plant present lower value.

16. Pest and disease infestation
Brinjal with a pest “28-spotted ladybird and change in spectral signature
Forest Research Institute, Dehradun Acharya et al., (2015)

17. College of Engineering, Lucknow Kumar (2012)
Drone technique

18. Sensors
College of Engineering, Lucknow Kumar (2012)

19. Visual sensors
• It is used for aerial mapping
and imaging
• Plant counting
• Surveillance
• Emergency response
• Surveying and Land use
application
College of Engineering, Lucknow Kumar (2012)

20. Multispectral sensors
• They range in numbers
of bands.
• Multispectral Sensors
are widely used in:
Plant health
measurement
Water quality
assessment
Vegetation index
Plant counting
College of Engineering, Lucknow Kumar (2012)

21. Thermal sensors
• Thermal sensors are best utilized in:
 Heat signature detection
 Livestock detection
 Surveillance and security
 Water temperature detection and water source detection
 Emergency response
College of Engineering, Lucknow Kumar (2012)

22. LIDAR sensors
• 270º scanning
• Useful in 3D digital surface
modeling.
• Surface variation detection and
flood mapping.
• Penetrates through vegetation:
It can perform plant height
measurement by collecting
range information from the
plant canopy and the ground.
College of Engineering, Lucknow Kumar (2012)

23. Hyper spectral Sensors
Hyper spectral sensors are widely used in:
• Plant health measurement
• Water quality assessment
• Vegetation index calculation
• Full spectral sensing
• Spectral research and development
• Mineral and surface composition surveys
College of Engineering, Lucknow Kumar (2012)
FAO, 2011 and Shanmugapriya et al., (2010)

24. How drones can be used in Agriculture
Crop monitoring Chemical applications
Land & building management Crop & livestock inventory
management
College of Engineering, Lucknow Kumar (2012)

25. Conclusion
Remote sensing is useful in assessing various abiotic
and biotic stresses in different crop and in detecting and
management of various crop issues even at small farm
holdings. To effectively utilize the information on crops for
improvement of economy there is need to develop decision
support system based on available information on various
crops derived from remote sensing and GIS approaches.
The governments can use remote sensing data in order to
make important decisions about the policies they will adopt
or how to tackle national issues regarding agriculture.

26. Remote Sensing Geographic Information System

27. • CCD (Charge couple detector): VNIR
• LISS (Linear imaging self scanner): SWIR
• PAN (Panchromatic camera)
• AWiFS (Advanced wide field sensor)

28. Hyper Spectral Remote Sensing Applications in Soil Research : Techniques, ISRO, Ahemdabad
Spectroscopy
• It is technology of detection and analysis of absorption feature of chemical bonds to
identify materials and their composition.
• Specific chemical compounds has specific absorption feature at specific wavelength,
hence narrow bandwidth helps to pick those specific absorption feature unlike
broadband.
• Sensors are capable to acquire the strength of reflection and radiation in each
wavelength. From the observation data, we can understand the status of forests,
ocean pollution and desertification.
• Also, the strength of reflection and radiation differs according to the condition of
objects, such as type of plants, status (dead, weak, etc.) or contamination of water.
Spectral indices used in
determination of
relationship between
vegetation and reflectance.