Recent advances on application of remote sensing in fruit production of important fruit crops

1. DOCTORAL SEMINAR
on
Recent advances on application of remote sensing in fruit
production of important fruit crops
COURSE NO. - FSC 691
CREDIT HOURS- 1(0+1)
Department of Fruit Science Indira Gandhi Krishi Vishwavidyalaya, Raipur
Seminar Incharge
Dr. Prabhakar Singh
Professor and Head
Department of Fruit Science,
College of Agriculture,
Indira Gandhi Krishi
Vishwavidyalaya, Raipur (C.G.)
Presented by,
Diksha
Ph.D. (Hort.)
First Year
(Fruit Science)

2. Introduction
Historical perspective
Principle of remote sensing
Applications of remote sensing in fruit crops
Stages involved in remote sensing system
Platforms for remote sensing system
CONTENTS
Types of sensors
Sensors used in fruit crops
Remote sensing organizations
Research findings
Government Initiatives
Constraints of remote sensing
Conclusion
References

3. Remote sensing
• Remote - Something which is far away
• Sensing – Getting information or getting data
India’s national remote sensing agency (NRSA) defined as: “ Remote
sensing is the technique of deriving information about objects on the
surface of the earth without physically coming into contact with them.’’
• Remote sensing is science of
Acquiring
Processing
Interpreting

4. Historical perspective
• 1860’s - First aerial photos from balloons
• 1940’s - 50’s - widespread use of aerial photos (WWII)
• 1960’s - First satellite images (ERTS)
• 1970’s - Landsat MSS, airborne scanners
• 1980’s - Landsat TM, AVHRR (Advances very high resolution radiometer)
• 1988 ‘s - First remote sensing in India was lunched , named as IRS.
• 1990’s - Airborne digital cameras, RADAR, IRS C&.D, release of
classified data
• 2000’s - New satellites, improvements in airborne digital cameras, LIDAR
• 2010’s - Higher spatial & spectral resolution, extensive use of LIDAR,
Hyperspectral imagery , UAV’s
• The term ‘’Remote Sensing’’ first used in united states in the 1950’s by Mr.
Evelya Pruill.

6. Principle of Remote Sensing
Source- Paul R. Beumann, 2010

7. Stages involved in remote sensing system
1. Energy source or illumination (A)
2. Radiation and the atmosphere (B)
3. Interaction with the target (C)
4. Recording of energy by the sensor (D)
5. Transmission, reception, and processing (E)
6. Interpretation and analysis (F)
7. Application (G)

8. Platforms for remote sensing system
Platforms are nothing but vehicles or carriers
of remote sensing. Mainly there are three
types of platforms.
Ground Based:
These includes the use of vehicles, Tripod
stands and towers. These are mounted with
cameras, and sensors at different heights.
Airborne:
It includes the use of aeroplanes,
helicopters, drone and balloons. It is not
cost effective one and occupies only small
areas.

9. Space Borne:
In space-borne remote sensing, sensors are mounted onboard a spacecraft
(space shuttle or satellite) orbiting the earth. Space born or satellite platform
are onetime cost effected but relatively lower cost per unit area of coverage.
• Earth synchronous satellites
• Sun synchronous satellites

10. Source-Moran et al.,1991

11. There are two basic types of sensors:
Passive Sensor:
In this type solar energy is used as a
source of energy. This operates only in
day condition whenever sunlight is
available.
• Camera without flash
• Thematic Mapper (TM)
• Landsat satellite.
Active sensor:
As name indicates this requires artificial
source of energy. It can be operate in
both day and night condition.
• Camera flash
• RADAR
• Topographic LIDAR laser

12. Vegetation indices
• Vegetation indices Biophysical features of plants can be characterized
spectrally by vegetation indices.
• NDVI index- used most frequently to determine the condition,
developmental stages and biomass of cultivated plants and to forecasts their
yields.
• It has become the most commonly used vegetation index ,Wallace et al.
(2004). Multispectral and hyper-spectral aerial and satellite imagery helps
in creating NDVI maps, which can differentiate soil from grass or forest,
detect plants under stress, and differentiate between crops and crop stages.

14. Global positioning system (GPS)
• GPS is “a network of satellites that continuously transmit coded
information, which make it possible to precisely identify locations
on earth by measuring distance from the satellites’’
• GPS provide the accurate positional information, which is useful in
locating the spatial variability with accuracy.
Some other tools in precision farming

15. Geographical information system (GIS)
• A geographical information system is a computer system capable of
capturing, storing, manipulation, and displaying spatially
information . Intermediate step because it combine the data collected
based on sampling regimes, to develop the process models, expert
system etc.
Spatial data GIS Computer

16. There are many types of sensors used in fruit crops
Sensor: These are the devices that receive electromagnetic radiation and
convert it into signal that can be recorded or displayed as either
numerical data or an image.
1. Microwave radiometer
2. Spectrometer
3. Radar
4. Satellites also act as sensors
5. Digital camera (RGB)
6. Multispectral camera
7. Infrared thermal imager
8. Hyperspectral camera
9. LIDAR

17. UAV/Drone with Digital Camera

18. Hyper spectral sensor

19. View of Multispectral sensor image
Source – Bay Spec, 2022
San Jose, USA

20. Thermal Infrared Imaging Sensor
Source –T.C Liu.,2012

21. LIDAR crop classification with Drone
Source –Adriaan Jacobus prins, 2021

22. Crop Devices/Platforms Purpose Type of Remote
Sensing
Apple Green seeker 505, tetra
cam ADC
Weed biomass evaluation Active and passive
remote sensing
IRSA WiFS Orchard characterization Satellite remote
sensing
Orange and
pineapple
IRS-P Satellite sensor,
LISS-III
Soil site suitability analysis Satellite remote
sensing
Peach Spectra radio diameter Mite damage Aerial remote
sensing
Nectarines
and peaches
Field spectrometer ASD
with reflectance of 400 -
1100nm
Detecting water stress effects
on fruits
Aerial remote
sensing
Mango IRS-P Satellite Yield estimation Satellite remote
sensing
Grapes Digital multispectral
sensors
Zonal vine yard management Aerial remote
sensing
Different types of remote sensing systems and sensors used in fruit crops
Source -Appani Laxman Kumar et al.,2021

23. Applications of Remote Sensing in fruit crops
 Assessment of crop area
 Delineation of orchards and spatial analysis
 Determination of fruit yield
 Quantification and scheduling of precise and proper fertilizer,
irrigation needs and application of pesticides
 Identification of diseases and pest attacks
 Weather study for site specific crops management in fruit crops
 Detection of growth and health of orchards on a larger scale.
 Land suitability assessment for crops
 Disaster management
 Precision horticulture

24. Land suitability assessment for orchard
Source – Rajendra Zolekar et al.,2018

25. Identification of diseases and pest attacks area

26. Disaster management

27. Some key application of remote sensing in fruit crops
1) Estimation of cultivable land area and mapping of orchards:
Remote sensing systems have the capability of providing regular,
synoptic, multi-spectral and multi-temporal coverage of a
cultivable area.
 For example Apple orchard area was estimated using remote
sensing and agro-metrology land based observation in Pulwama
district of Kashmir valley. Landsat and AWIFS digital data were
used to monitor and estimate acreage under apple orchards.
2) Recommended dose of fertilizer application: The site-specific
grove management by variable rate delivery of inputs such as
fertilizers on a tree size basis could improve horticulture crops
profitability, reduce fertilizer wastage and provide environmental
protection.
Source- Appani Laxman Kumar,2021

28. 3) Detection of Water Stress: Remote sensing technology can also
help in identification of water stress by change in leaf colour. Many
techniques such as high spatial resolution multispectral and thermal
airborne imagery were used to monitor crown temperature and the
Photochemical Reflectance Index (PRI) in peach orchards.
4) Quality assessment in grape using remote sensing : In grape wine
quality was assessed by vigour of vine (Johnson et al., 2001).
Remote sensed vegetation index imagery was used to establish sub-
block management zones in a three hectare commercial vineyard of
chardonnay wine-grapes.
5) Identification of Pest and Diseases in Fruit : Remote sensing
techniques can decrease pest monitoring costs in orchards. The
spider mite damage in orchards is evaluated by measuring visible
and near infrared reflectance of leaves and canopies in peach
orchards in California. (Eike L. et al.,2009)

29. 6. High resolution remote sensing imagery in fruit crops
The potential of the narrow-band TBVI (two-band vegetation
index) derived from airborne hyperspectral imagery to predict
citrus fruit yield was examined in Japan (Ye et al., 2008).
• The microwave backscatter response of pecan tree canopy
samples to estimate pecan yield in situ using terrestrial radar
was studied (James et al., 2013).
7. Soil salinity detection using thermal imaging
Soil salinity causes severe environmental degradation that
impedes crop growth and overall regional production. Soil
salinity information can be extracted from thermal imageries
as emitted radiance can provide subsoil information.

30. Remote sensing organizations
These are some remote sensing organizations at national and international level
1. ISPRS- International Society for Photogrammetry and Remote Sensing(France)
2. NASA- National Aeronautic and Space Administration (USA)
3. ESA- European Space Agency (Europe)
4. NASDA- National Space Development Agency (Japan)
5. CSA- Canadian Space Agency (Canada)
6. ISRO- Indian Space Research Organisation (Banglore, Karnataka)
7. NRSA- National Remote Sensing Agency (Balanagar, Hyderabad)

31. Research findings

32. Case study#1
Research title: Mango yield mapping at the orchard scale based on
tree structure and land cover assessed by UAV.
Major findings:
 To estimate and map tree species, structure, and yields in mango
orchards of various cropping systems in the Niayes region, West
Senegal.
 Tree structure parameters (height, crown area and volume), species, and
mango cultivars were measured using unmanned aerial vehicle (UAV)
photogrammetry and geographic, object-based image analysis.
 This procedure reached an average overall accuracy of 0.89 for
classifying tree species and mango cultivars.
 Models reached satisfying accuracies with R2 greater than 0.77 and
RMSE% ranging from 20% to 29% when evaluated with the measured
production of 60 validation trees.
Author: Julien Sarron et. al. 2018

33. Orchard yield mapping outputs
.
A) UAV- acquired
RGB orthomosaic
image
B) GEOBIA Land
cover map
C) Canopy height
model (CHM)(in
meter)
D) Mango cultivar
yield map (in Kg
per tree).

34. Case study#2
Research title - Geospatial technology for acreage estimation of
natural rubber and identification of potential areas for its
cultivation in Tripura state.
 Potential of temporal and multi-resolution satellite datasets were
explored in this project for inventory of natural rubber in Tripura.
 The study revealed that natural rubber showed distinct spectral signature
on multi-date LISS-III data and it could be delineated using temporal
NDVI profile.
 The satellite analysis revealed about 48,033 ha of natural rubber (more
than 3 years old) in the state as of december 2011.
 The mapping accuracy for the older mature natural rubber was 96.15
per cent indicating the utility of RS data for NR mapping and
monitoring.
Author - Mr. Uday Raj et.al 2012

37. Case study#3
Research title: Spectral discrimination of healthy and malformed
mango panicles using spectroradiometer.
Research findings–
• The potential of using hyperspectral reflectance data to discriminate
healthy and malformed mango panicles, both under field and
laboratory conditions was evaluated.
• The spectral reflectance of both malformed and healthy panicles were
collected using ground based portable ASD FS3 spectroradiometer.
• Among all the indices Normalized Difference Vegetation Index
(NDVI) was found to be the best parameter in differentiating the
healthy (0.495) and malformed (0.751) mango panicles significantly
under laboratory conditions.
• Hence, it can be concluded that spectral reflectance based indices will
be a potential tool in the identification of malformed mango panicles
spatially through remote sensing data.
Author - A. Nagaraja et al., 2014

38. Vegetative indices of healthy and malformed panicles of mango at
laboratory (A) and field conditions (B).
Index NDVI
A
Laboratory condition
B
field conditions
Healthy panicle 0.495 0.503
Malformed panicle 0.751 0.780

39. Case study#4
Research title - Citrus canker detection using hyperspectral
reflectance imaging and PCA based image classification
method.
Research findings –
• This research demonstrated that hyperspectral imaging
technique could be used for discriminating citrus canker from
other confounding diseases.
• The overall accuracy for canker detection was 92.7%.
• Four optimal wavelengths (553, 677, 718, and 858 nm) were
identified in visible and short-wavelength near-infrared region
that could be adopted by a future multispectral imaging
solution for detecting citrus canker on a sorting machine.
Author -Jianwei Qin et al.,2008

40. Images of a cankerous samples at selected wavelengths from
400 to 900 nm
Hyperspectral reflectance images of grapefruit samples

41. Images of normal and other diseased skin conditions at 600nm

42. Reflectance spectra of grapefruit samples with normal and diseased skin conditions
Reflectance between canker and other skin conditions was evident in spectral
region between 550 to 850 nm, in which relative reflectance of cankerous and
marketable samples were in the range 15 -42% and 37-65% respectively.

43. Government Initiatives
FASAL (Forecasting Agricultural output using Space, Agro-
meteorological and Land-based observations)
Launched in 2006.
• The main aim was to collect monsoon data using remote sensing,
economic data and monitoring of crop when growing.
• A team of ISRO/Department of Space, State Remote Sensing
Applications Centers, State Agricultural universities and many other
institutions are working on FASAL.
• Remote sensing component of FASAL- handled by the Ahmedabad
- based Space Application Centre of the Indian Space Research
Organization.

44. NCFC (National Crop Forecast Centre)
• Launched in April 2012
• Provide estimates of crop output and assess the drought situation in
the country through latest technologies.
• Located at the New Delhi.
• Equipped with image-processing facilities, laboratories and
software.
• Named after the well known Indian Statistician P. C. Mahalanobis,
founder of the Indian Statistical Institute, who played a significant
role in evolving methods to estimate crop yields.

45. CHAMAN (Coordinated Horticulture Assessment and
Management using Geoinformatics)
• Implemented by Mahalanobis National Crop Forecast Centre
(MNCFC) in collaboration with ISRO Centres and 12 state
horticulture departments, NHRDF, IMD, ICAR Centre and State
Remote Sensing Centers is likely to be completed in march 2018.
• Envisages use of satellite remote sensing data for area and production
estimation of 7 horticultural crops (Potato, Onion, Tomato, Chili,
Mango, Banana and Citrus) in 12 major states in 180 districts.
• The programme also uses GIS (Geographical Information System)
tools along with remote sensing data for generating action plans for
horticultural development (site suitability, infrastructure development,
crop intensification, orchard rejuvenation, aqua-horticulture, etc.).

46. Advantages of remote sensing
1. Remote sensing is a fast process and it can survey large and
inaccessible areas in a short time.
2. Once Remote Sensors have collected data, it can be used and
analyzed multiple times for different applications.
3. Remote sensing technology like LIDAR collects point cloud data,
this data can be quickly and easily analyzed with point cloud
software.
4. Remote Sensors measure reflected light either natural sunlight or a
light pulse. This light is harmless to objects, vegetation, human and
environment.
5. Remote Sensing allows for easy collection of data over a variety of
scales and resolutions.

47. Constraints of Remote Sensing in India
• Small size of land holdings
• Lack of success stories
• Infrastructure and institutional constraints
• Lack of local technical expertise
• Heterogeneity of cropping systems
• High initial investment
• Issues with satellite data
 Not viable for small land holdings
 May not be effective for pest attack information due to inherent
delay in providing data
 Limitation of predictive model development
 Data degradation due to atmospheric disturbances

48. Conclusion
• Remote sensing technology has developed from balloon photography to
aerial photography & then advanced to multi-spectral satellite imaging.
• Precision farming in many development countries including India is in its
infancy but there are numerous opportunities for adoption.
• Radiation interaction characteristics of earth and atmosphere in different
regions of electromagnetic spectrum are very useful for identifying and
characterizing earth and atmospheric features.
• Remote sensing raising yields and economic returns on fields with
significant, variability and for minimizing environmental degradation.
• Helps in reducing monitoring costs, enhances resource use efficiency,
lowering total production cost and Increases profit.

49. References
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Jacob, J., & Raj, U. (2012). Geospatial Technology For Acreage Estimation of Natural
Rubber and Identification of potential Areas for its Cultivation in Tripura
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Institute of India Rubber Board, Min. of Commerce & Industry, Govt. of India.
James A, Hardin PR, Weckler CL. Microwave backscatter response of pecan tree
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52. Thank you