A brief presentation on the scenario of the application of remote sensing technology in fruit crop development and production

1. Application of Remote Sensing in fruit
production and development
Kiran Patnaik
04 FSC/Ph.D./16
Department of FSHT , Orissa University of Agriculture and Technology, Bhubaneswar

2. Remote
Sensing : An
eye in the sky
for
agriculture

3. Agenda
Introduction to Remote Sensing
Brief history
Elements of Remote Sensing
Principle of Remote Sensing
Components of Remote Sensing systems
Application in fruit crops
Role of vegetation indices in Remote Sensing
Research findings
Government initiatives w.r.t. Remote Sensing
Scope of Remote Sensing
Future thrusts
Conclusion
References
3

4. Introduction
Remote sensing is the collection of reliable information about
an object without being in direct physical contact with the
object.
Remote Sensing is science of
 acquiring
 processing
 interpreting
images and related data that are obtained from ground-based,
air-or space-borne instruments that record the interaction
between matter (target) and electromagnetic radiation.
4

5. Historical perspective
1860’s - First aerial photos from balloons
1940’s - 50’s - widespread use of aerial photos (WWII)
1960’s - First satellite images
1970’s - Landsat MSS, airborne scanners
1980’s - Landsat TM,SPOT, AVHRR
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
5

6. Elements involved in Remote Sensing
Energy Source or Illumination (A)
Radiation and the Atmosphere (B)
Interaction with the Object (C)
Recording of Energy by the Sensor (D)
Transmission, Reception and Processing (E)
Interpretation and Analysis (F)
Application (G)
6

7. Principle of Remote Sensing
7

8. Remote Sensing Sensors
A sensor system records electromagnetic (EM) radiation detected
as a combination of reflected solar radiation and emitted radiation
by an object.
Sensors
Active SensorsActive Sensors Passive
Sensors
Passive
Sensors
Artificial Energy SourceArtificial Energy Source Natural Energy SourceNatural Energy Source
e.g. Radar Systems
SLAR, SAR
e.g. Radar Systems
SLAR, SAR
e.g. Sensors on Satellites
Landsat, SPOT
e.g. Sensors on Satellites
Landsat, SPOT
8

9. Passive Sensor vs Active Sensor
9

10. Remote Sensing platforms
10

11. Remote Sensing organisations
11
1. ISPRS- International Society for Photogrammetry and Remote
Sensing
2. IGARSS-International Geoscience Remote Sensing symposium
3. NASA- National Aeronautic and Space Administration (USA)
4. ESA- European Space Agency (Europe)
5. NASDA- National Space Development Agency (Japan)
6. CNES- Centre National d’stedesSpatiales (France)
7. DARA- German Space Agency (Germany)
8. CSA- Canadian Space Agency (Canada)
9. ISRO- Indian Space Research Organisation (Banglore,
Karnataka)
10.NRSA- National Remote Sensing Agency (Balanagar,
Hyderabad)

12. 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
12

13. 13
a) Red colour shows cultivated
plants on satellite image
b) Problems inside the agricultural fields

14. Vegetation indices
Biophysical features of plants can be characterized spectrally by vegetation indices.
Calculated as ratios or differences of two or more bands in the VIS, NIR and SWIR
wavelengths.
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), Calvao 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

15. Vegetation indices Contd.
15

16. Vegetation indices Contd.
16

17. Research findings
17

18. Research findings Case Study #1
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.
18

19. Research findings Case Study #1
19
Temporal LISS-III data showing Natural Rubber plantations

20. Research findings Case Study #1
20
NDVI profile of different vegetation classes

21. Research findings Case Study #1
21Temporal LISS-III data along with classified data showing NR Plantations

22. Research findings Case Study #2
Panda et al.(2016) studied the use of advanced image
processing technique and high resolution multispectral
imaging to distinguish blueberries orchards from other land
uses.
Very high resolution 1-meter multi-spectral NAIP imagery
was used to identify blueberry orchards in three southeastern
counties (Bacon, Brantley, and Camden) in Georgia.
Advanced image processing techniques, including principal
component analysis and self-organizing map (SOM) neural
network image segmentation technique, were used in this
study.
22
Blueberry Orchard Delineation with High-Resolution Imagery

23. Research findings Case Study #2
23
Blue berry orchards surrounded by forest , shrubs and grasses

24. Research findings Case Study #2
24

25. Research findings Case Study #3
25
Remote sensing-based monitoring of chlorophyll dynamics in fruit
orchards
Pieters et al.(2017) studied the dynamics in the
canopy chlorophyll content within a pear orchard which
was monitored by creating chlorophyll maps of the orchard
from remotely sensed images at different key moments of
the growing seasons. These chlorophyll dynamics were
related to the productivity of the trees in orchard.

26. Research findings Case Study #3
26

27. Research findings Case Study #3
27
Distribution of the canopy chlorophyll content (µg/cm2
) of
productivity class 1 (left) and productivity class 4 (right) for May,
July and August. [Pieters et al.(2017)]

28. Research findings Case Study #3
28
RPAS images of the orchard in May (left) and in August (right), on
which the experimental trees are indicated by the circles. The outer
and inner circles representing the productivity and chlorophyll
classes repectively. [Pieters et al.,(2017)]

29. Research findings Case Study #4
Madeleine et al. (2016) investigated the use of multiple view-
point fruit detection, tracking and counting as a means to
measure and map the quantity of fruits in a mango orchard. A
total of 522 trees and 71,609 mangoes were scanned on a
Calypso mango orchard near Bundaberg, Queensland,
Australia, with 16 trees counted by hand for validation, both
on the tree and after harvest.
The results show that single, dual and multi-view methods can
all provide precise yield estimates, but only the proposed
multi-view approach can do so without calibration, with an
error rate of only 1.36% for individual trees. 29
Image Based Mango Fruit Detection, Yield Estimation Using
Multiple View Geometry

30. Research findings Case Study #4
The robotic platform Shrimp (a) with labels to indicate the
locations of the sensors used in this paper; a map of the mango
orchard test block (b), with yellow pins marking the 18 trees that
were selected for ground-truth field and harvest fruit counts. 30
(a) The Shrimp Robotic Platform (b) Mango Orchard Site

31. Research findings Case Study #4
31
Fruit Tracking and Counting
(a) Example with Real Data (b) Close-up View

32. Research findings
32
Shrivastava et al.(2006) performed a study in Florida to delineate
citrus groves for economic assessment, using Landsat Enhanced
Thematic Mapper Plus (30 m) imagery. Their results showed a
significant correlation between citrus production/income with
remotely sensed imagery-derived citrus area coverage.
Sharma et al.,(2007) used Indian Remote Sensing Satellite (IRS)
LISS III and IRS AWiFS (23 m and 55 m, respectively) imagery
data to develop an apple orchard database for the entire state of
Himachal Pradesh and were successful in the delineation using a
relatively low resolution imagery.

33. Research findings
33
The same low resolution IRS imageries were also used for identifying and
prioritizing the suitable sites for passion fruit plantations in the hilly districts of the
north-eastern states of India.
O’Connel et al.,(2005) used remotely-sensed high resolution aerial photographs to
identify the tree canopy of a peach orchard for yield forecasting and the estimation
of crop water requirements. In their case, they needed to identify individual trees to
perform SSCM and hence they used high resolution images.
Torres et al.,(2008) conducted a study to distinguish olive tree orchards using
remote sensing images by clustering assessment techniques. They used 0.25- to 1.5-
m resolution imageries to perform the advanced SSCM process for an olive orchard
that was only 2 hectares in size.

34. Government Initiatives
34
Launched in 2006.
Reliable crop prediction mechanism.
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.
Increase crop yield with use of Remote Sensing:
FASAL (Forecasting Agricultural output using Space, Agro-
meteorological and Land-based observations)

35. 35

36. Government Initiatives Contd.
36
Provide real-time information about the prevalence and severity
of droughts at district and sub-district level in 13 key
agricultural states.
Prepares fortnightly reports on droughts using advanced wide-
field sensors of satellites like Resourcesat-1, IRS 1C and IRS
1D.
NADAMS (National Agricultural Drought Assessment and
Monitoring System)

37. Government Initiatives Contd.
37
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 base Pusa.
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.
NCFC (National Crop Forecast Centre)

38. Government Initiatives Contd.
38
Implemented by Mahalanobis National Crop Forecast Centre (MNCFC) in
collaboration with ISRO Centres (SAC & NRSC) and 12 state horticulture
departments, NHRDF, IMD, ICAR Centre and State Remote Sensing
Centres.
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.).
CHAMAN (Coordinated Horticulture Assessment and
Management using Geoinformatics)

39. Government Initiatives Contd.
39

40. Government Initiatives Contd.
40
Potato Production Forecast at National/State Level and Final
forecast (F2) at District Level for 5 states (Bihar, Gujarat,
Punjab, Uttar Pradesh, West Bengal)
Mango and Citrus Orchard mapping: Using LISS III , LISS IV
and Cartosat dataset object based/Pixel-based in selected
districts of Bihar, UP & Punjab.
Post-harvest infrastructure planning by assessing the potential
of cold storage capacity for fruits and vegetables in Bihar state.
Aqua-horticulture: Remote sensing based assessment of
wetlands for Makhana (Foxnut) cultivation in Darbhanga
district of Bihar.
Major achievements of the project include:

41. Scope of Remote Sensing
41
There is a great potential for airborne hyper-spectral imagery
use in estimating fruit yield and detecting plant stress as
supported by several studies [Cross et al.,(2008)].
Polo et al. (2008) have used a low-cost tractor-mounted
scanning Light Detection and Ranging (LIDAR) system for non-
destructive recordings of tree-row structure in orchards and
vineyards to determine the tree-row volume and total surface
area (crown cover).
The use of LIDAR imagery data for horticultural SSCM has an
immense scope because it can determine plant heights along
with spectral characteristics.

42. Scope of Remote Sensing Contd.
42
According to Schupp et al. (2009), imaging technology has
the potential to cross multiple areas of tree fruit production,
such as:
Crop load assessment
Blossom or green fruit counts
Yield estimation
Determination of insect presence or disease infection and
associated eradication
Soil moisture content determination for enhanced irrigation
system design
Estimation of fertilizer, pesticide, and herbicide application
rates
Development of assisted or automated pruning and harvesting
strategies.

43. Future Thrusts
Keeping in view the agricultural scenario in developing
countries, the requirement for a marketable RS technology for precision
agriculture is the delivery of information with the following
characteristics:
Low turn around time (acquired, corrected and processed) ~ 24-48 hrs
Low data cost
High spatial resolution (at least 2m multi-spectral for 1 ha field size)
High spectral resolution (10-20 nm for retrieving biophysical
parameters)
High temporal resolution (at least 5-6 dates per season)
Delivery of analytical products in simpler format
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44. Future Thrusts
With current satellites, one can see areas that are 30 meters x 30 meters (11.1
measurements/ha), 23 x 23 meters (18 measurements/ha), 10 x 10 meters (100
measurements/ha) and 5 x 5 meters (400 measurements/ha). With future satellites,
we will be receiving data that have a variety of spatial resolutions that in some cases
will be as detailed as 1 x 1 meter or over 10000 data points per hectare.
Integration of biophysical parameters (such as Leaf Area Index or temperature)
derived from high-resolution satellite based remote sensing data, with physical crop
growth modeling towards an operational decision support system for precision
farming.
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45. Advancements in Remote Sensing technologies
Unmanned aerial vehicles (UAVs), commonly known as drones.
Drones are remote controlled aircraft with no human pilot on-board.
capable of collecting very high-resolution imagery below the cloud
level, with much more detail than the satellite imagery.
Easy to use as the UAV essentially flies itself.
Data processing applications are becoming less expensive and
easier to use.
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46. 46

47. Hyperspectral image from UAV
47

48. FarmBeats: An IoT Platform from Microsoft for Data-
Driven Agriculture
48
Farmbeats focuses on four aspects:
Farming-yield estimation
Precision irrigation
Pest-infection
Fertilizer application
Data required to advice on those four aspects are acquired from
the farm and collected on cloud using TV White Space technology.
At the farm level a white space device such as a router is installed
by the farmer that would facilitate data collection on soil moisture,
nutrient content, etc.

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50. Constraints of Remote Sensing in India
Small farm size
Lack of success stories
Infrastructure and institutional constraints
Lack of technical expertise
Heterogeneity of cropping systems
Issues with satellite data
Not viable for small land holdings and mixed cultivation
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
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51. Conclusion
Precision farming in many developing countries including
India is in its infancy but there are numerous opportunities for
adoption.
Progressive Indian farmers, with guidance from the public
and private sectors, and agricultural associations, will adopt it
in a limited scale as the technology shows potential for raising
yields and economic returns on fields with significant
variability, and for minimizing environmental degradation.
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52. References
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Tabb A, Park J, Slaughter D, Johnson S, Landers A, Reichard G,
Singh S, Bergerman M, Kantor G and Messner W. 2009. Speciality Crop
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Calvao T and Palmeirim J M. 2004. Mapping Mediterranean scrub with satellite
imagery: biomass estimation and spectral behaviour. International Journal
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Cross J V and Walklate P J. 2008. The UK PACE scheme for adjusting the dose
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O’Connell M G and Goodwin I. 2005. Spatial variation of tree cover in peach
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Panda S S, Hoogenboom G and Paz J. 2016. Blueberry Orchard Delineation
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Pieters C, Vanbrabant Y, Tits L and Somers B. 2017. Remote sensing based
monitoring of chlorophyll dynamics in fruit orchards:linking vitality to
productivity. Retrieved from Limo Database(http://limo.libis.be).

53. Contd…
Polo J R R, Sanz R, Llorens J, Arnó J, Escolà A, Ribes-Dasi M, Masip J, Camp F, Gràcia
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Thank you