Using remote sensing technologies such as drones, satellites, and mobile apps can improve crop cutting experiments (CCEs) conducted for the Pradhan Mantri Fasal Bima Yojana (PMFBY) agricultural insurance program in India. A pilot project in Maharashtra called TOPS integrated satellite imagery, weather data, and a mobile app to automatically select sampling locations, estimate crop yields virtually, and produce high resolution yield maps. Initial results found crop yield estimates were within 8-22% of independent assessments. The project demonstrated resource optimization through intelligent sampling and yield monitoring at village-level scales. Wider adoption of such technologies could help make CCEs more efficient and yield estimates more accurate.

1. Using Remote Sensing Technologies to Improve
Sampling
Opportunities in the new Pradhan Mantri Fasal Bima Yojana (PMFBY)
Agriculture Insurance Program
Workshop at IFPRI, New Delhi, 21st December 2016
Mangesh Niranjan Patankar

2. 2
Agenda
• Where are we? CCEs in the current context of PMFBY
• How can we improve?
• Optimization, experiences from pilot
• Way forward

3. Where are we?
CCEs in the current context of PMFBY
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4. CCEs in the context of
PMFBY
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Standing Crop (Sowing to
Harvesting) - Area Yield
Index Based Coverage
Prevented Sowing
Mid Season Calamity
Post Harvest Coverage
Localized Calamity
Claim given in case of ‘failed’ sowing of major
crops due to inclement weather condition. Cover
triggers if more than 75% area is affected.
Provision for ‘on account’ settlement of claim in
case of a wide spread calamity affecting the crops.
Such payment would occur only if 'estimated' loss
is more than 50%, with actual claim amount
capped at 25% of 'potential claim'
‘Indemnity style’ claim assessment. Farmer needs
to file for the losses happening due to localized
perils like hailstorm. Coverage reflects expenses
occurred till the event
Forms core ‘index style’ component of the product.
‘Crop Cutting Experiments’ to be carried out at the
‘notified area’ to determine the ‘representative
sample yield’ based on which yield index for the
given territory is decided. Such yield is then
matched with the ‘threshold yield’, which is
70%/80%/90% of the average historical yield
(excluding maximum 2 calamity years). Yield
shortfall is paid as claim at a pre-determined rate.
‘Indemnity style’ claim assessment. Covers post
harvest losses suffered from crops which are
allowed to dry in cut and spread condition on the
farms, within maximum 2 weeks of harvesting.
Perils included – cyclone, cyclonic rains and
unseasonal rainfall.

5. Existing Mechanism
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Random selection of
field (well before the
harvesting begins)
Locating and marking
experimental plot in
the specific field
Harvesting the CCE
plot
Threshing and
harvesting of crop
Weighing, Drying and
Weighing dry weights
(depending on the
crop)
Publishing plot data,
extrapolating yield
index based on
average yields for
number of plots
Granularity
Costs,Resources
Not much
emphasis
on agro-climatic
seggregation

6. Existing Mechanism
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Party Function
Local Revenue Department
Officer (Patwari)
Plot Selection, Conducting CCEs,
Reporting in a specific format (Form – 2)
Agriculture Department User of the data for insurance and other
purposes
Monitoring Agency Deployed
by Insurer
Coordination with Patwari, Monitoring
actual CCEs based on targets
Insurer, Reinsurer Ultimate user of the data for calculation
of claims/disbursement of claims
State Level Coordination
Committee on Crop Insurance
First level of contact for any clarifications
on notification, claim etc.
Department of Agriculture and
Cooperation and Farmers’
Welfare (DAC & FW)
Ultimate dispute resolution authority.
Decision binding on state, insurer, bank
and farmers

7. PMFBY: Where we are… Emphasis on Use of Technology
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• Use of drones for reliability, accuracy, speed of
conducting CCEs
• Online transmission of data to avoid delays in
claims assessments and payouts
• Use of mobile for real time transmission of CCE
data with GPS, date & time stamping
• Use of these technologies to settle claims based
on satellite images/derived products once the
correlations are established
• Cost of technology including purchase of
hardware devices like smart phones will be
equally borne by state & central Govt., at 50:50
basis.
• Technical Advisory Committee to assist state
government and insurers on technology
intervention
• NITI Aayog deliberating on the CCE optimization
mechanism
Central Crop Insurance Portal
http://agri-insurance.gov.in/Login.aspx

8. How can we improve?
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9. • Minimize the number of experiments. E.g. even for a circle level assessment, state of Maharashtra
needs about 55,000 crop cuttings.
• Minimize time lags in publishing the crop yield statistics, generated for the cropping period
• Predict sowing failure and associated economical loss
• Determine accurate crop yield statistics at a granular level (micro level)
• Minimize on-site loss survey expenses and delays in case of localized calamities like hailstorm
• Detect early warnings from the field and implement an action plan accordingly for irrigation, agri-
credit and other agri-inputs
• Minimize insurance claim disputes by the use of objectively verifiable techniques
• Equip the stakeholders with necessary technological know-how (especially the government
machinery, insurers and agricultural research agencies)
Key Requirements
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10. Geographical Scope
 Limited to the State of Maharashtra
 14 districts for production of the yield statistics
 All the districts for production of the weather statistics (overall
40,000 villages)
Temporal Scope
Kharif 2012 to Rabi 2015
Project Partners
Swiss Re, Agriculture Insurance Company of India, Niruthi Climate
and Ecosystems, CRIDA, Government of Maharashtra
Special Assistance
Recognizing the long term utility of the concept, TOPS project
received an assistance from Government of Maharashtra, under its
PPP-IAD programme. Rest of the support was received from Swiss Re
and AICI.
Initial Deliverables (set in 2012)
1. Weather statistics – Produce historical (1982 to 2011) as well
as current (2012 to 2015) weather data statistics at village level
(for 40000 villages) for the whole state
2. Yield statistics – Prepare historical (2001 to 2011) as well as
current (2012 to 2015) yield statistics for selected regions at
village level for specific crops (jowar, bajra, cotton, soybean and
gram)
As a bi-product, it was observed that the state government also
benefited from the project as the project demonstrated that
considerable resource optimization is possible using the smart CCE
sampling methodologies.
TOPS Project in the
State of Maharashtra

11. • TOPS (Terrestrial Observation and Prediction System) integrates extensive libraries
for data retrieval and preprocessing, algorithms for processing satellite data,
interpolation of meteorological data, application programming interfaces to facilitate
integration of multiple models, and numerous databases to archive and manage the
metadata associated with model inputs and model outputs.
• Satellite imagery is extensively used in the project to identify the crop and crop
conditions.
• The project also heavily uses handheld mobiles and a mobile app named CropSnap
for capturing GPS tagged images, crop booking, crop stage assessment and crop
yield estimation.
• The project uses cloud technology for the storage of data where the images are
interpreted through machine learning algorithms (artificial intelligence). The entire
acquisition and processing process is automated taking into account all the
communication issues in rural India.
• A dedicated portal was created to indicate the locations of CCEs, yield statistics and
contact information of the farmers. Software for dynamic sampling scheme was also
created.
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Further details - TOPS

12. • Create an intelligent sampling
scheme using TOPS technology to
determine the minimum number of
CCEs to be conducted
• Cluster villages in each circle into
low, medium and high yield
categories
• Conduct virtual CCEs with CropSnap
in each village
• Conduct CCEs manually at chosen
locations based on intelligent
sampling and virtual CCEs
• Assimilate crop yield data from the
CCEs into the satellite-based yield
maps to produce a final yield surface
for each village
• Collate crop yields for each village
and submit the data to Maharashtra
government
Methodology
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13. Manual
CCE’s
Camera
CCE’s
Panoramic
Close-up
Granular
CROP
SNAP
Photo
to
Yield
Yield
MACHINELEARNING&
ANALYTICS
Potential Yield
Calibrate
High Resolution Final Yield (0.5 ac pixels)
Refinement
High Res Satellite Data
High Res Weather Data
Crowdsourcing
Send Spreadsheets
Historical
Yield Models
Village-level yield monitoring using satellite, weather
and mobile data
CROP
SNAP
Steps in crop yield estimation
A unique example
where several
technologies
(Satellites/Sensors
and Data
Management) put to
use for tackling a
complex challenge!

14. • Conducted nearly 6000 CCEs spread across 14 districts during Kharif and Rabi
seasons of 2014 in selected crops. Further, in 2015, around 6000 CCEs were
conducted in 108 villages together in Kharif and Rabi. Data for gram, jowar,
bajra and soybean was found to be within 8%, 13%, 15% and 22% of the yields
assessed by independent methods.
• Intelligent sampling performed better during Rabi season when compared to
Kharif. Lowest savings were seen in Soybean and highest in Jowar.
Specific Recent Findings
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15. • Use of image based crop recognition can be operationalized once the
necessary models are in place – but it takes time!
• Accuracy can be an issue in the initial years, given that the initial data fed to
the models needs to be from the historical datasets available through
manual conventional crop cuttings – which may not be the best source for
such data
• Need an end to end system/portal, which takes care of
– smarter sampling,
– rapid yield assessment using manual as well as image based inputs and
– digital reporting of the yield
• Smooth adaptation by government is important to ensure that the efforts
are not limited to pilots
Way Forward…
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16. Example of potential expansion of village-level yield data to individual fields within the village. This process involves processing high-
resolution satellite data at sub-meter resolution to identify and tag individual fields
Future outlook: Farm-level crop yield estimation

17. Thank you…
Contact Details:
Mangesh Niranjan Patankar
Client Service Manager Agriculture, Assistant Vice President
Property & Specialty Underwriting
Swiss Re Services India Private Ltd., Mumbai, India
Direct: +91 22 6661 2153 Mobile: +91 77108 91100 E-mail: Mangesh_Patankar@swissre.com
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19. Legal notice
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