RMSI Cropalytics

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RMSI Cropalytics


MapsMap development and

maintenance for Navigation

NetworksNetwork design and maintenance for

Utilities and Communications

ModelingNatural Catastrophes, Climate

change, Agriculture and Natural

resources

AnalyticsGeospatial Big-Data, Location

intelligence

DigitizationLarge scale data

conversion and integration

We make the digital & the physical

world come together


RMSI Overview


RMSI’s Key Offerings in Agriculture

“GIS Solutions for Sustainable Agriculture and Food Security”

Agriculture

Information for

Supply Chain

Management

Feasibility

and Planning

Studies

Natural Resource

Information and

Management

Training and

Capability

Development

▪ Crop acreage

estimation

▪ Crop yield

modeling and

production

estimation

▪ Crop spatial

distribution

mapping

▪ Crop health

monitoring

▪ Baseline survey

and mapping

▪ Crop production

improvement

planning

▪ Water resources

development

planning

▪ Land use

development

planning

▪ Soil Survey

Mapping and

quality

Assessment

▪ Crop survey and

Spatial distribution

mapping

▪ Detailed land use

and land cover

mapping

▪ Crop pattern and

change detection

analysis

▪ Crop, soil and

land use survey

▪ RS and GIS

techniques for

agri-information

▪ Statistical

methods in natural

resource

management

RMSI Cropalytics - PInCER™Remote Sensing Based Crop Acreage &

Production Estimation & Crop Health

Monitoring


RMSI Cropalytics solutions benefit multi stakeholders across

the agriculture value chain

Reinsurers & Brokers

Crop Insurance Companies

Farming, Agri-Input &

Commodity Companies

Social Sector

Government & Developmental

Agencies

Helping Farming Community Through Advanced Analytics

Banks & Financial Institutions


PInCER™ (Profiler for Insured Crop Exposure and Risk)

RMSI Cropalytics PInCER™ is a

comprehensive crop data management

platform that:

“ We believe, technology can solve many challenges of

agriculture ”

Estimates farm-level yield (and therefore, income), remotely

Identifies loanee acreage on map and tracks crop health (analyzes portfolio risk) as the season progresses

Estimates loan-wise loss over hundreds of thousands of crop loans remotely

Draws a list of loanee farmers who are likely to be distressed and more importantly, unlikely to receive claim payouts

Identifies faulty crop loan applications


PInCER™ - Proprietary Model

Major Breakthroughs

▪ Model developed to stitch together

images from multiple satellites to

obtain cloud-free composite image

▪ Model developed for remote sensing-

based yield estimation accurate up

to 90%

▪ Damage functions developed for all

crops, all districts to forecast yield/

losses based on actual weather

▪ One of very few companies selected

by the Ministry of Agriculture,

Government of India, to carry out

satellite-based crop health and yield

estimation

DISTRICT / VILLAGE

WISE YIELD

Cadastral/Village boundaries

High resolution

satellite imagery

Land Record Data

Weather & hazards

database

Yields database

Pest & Disease attack

Village Mapped 6.5 Lac+

Yield Forecasted for 600+ Districts

Major Crops – 20+

Cereals: Rice, Wheat, Jowar, Maize, Corn

Pulses: Urad, Moong, Gram, Toor, Lentils. Sesame,

Chickpea, Pigeon pea

Cash Crop: Sugarcane, FCV Tobacco, Cotton, Mentha,

Oilseeds: Sunflower, Groundnut, Mustard, Soybean

Vegetable: Potato


PInCER™ Data Repository : Legacy Strength from RMSI

Sophisticated modeling requires

good data. RMSI’s years of

consulting work has helped us

build one of India’s best

agriculture databaseTIME

DEPENDENT DATA

Weather Forecast

Crop Classification

Crop Yield by IU (previous

Year

PROCESSED DATA

Cleansed & De-Trended Crop Yield &

Weather Data

Gridded Rainfall

ADMIN BOUNDARIES

States

District

Tehsil/Villages

Time independent data :

▪ Historical crop yield (8 to 20 years)

▪ Historical climate data including rainfall,

maximum temperature, minimum

temperature and relative humidity – 114

years

▪ Historical pest and disease attack event

data – 18 years

▪ Historical data on landslides (induced by

rainfall or earthquake) – 30 years

▪ Historical data of Cyclones – 200 years

▪ Historical data of Hailstorms – 50 years

▪ Soil characteristics - type

▪ Crop varieties

▪ Historical crop losses due to floods,

cyclone, drought, heat/cold wave, etc.

▪ Historical satellite imagery


Solution & Services

▪ In-Season Tracking: Satellite-based solution combined with on-ground intelligence, to

provide near to real-time update on acreage, yield, losses & crop progress along with its

health conditions during the season, to get early heads-up on distress hotspots

▪ Farmer Credit Worthiness: Helps determining faulty loan applicants and Portfolio Size &

Risk for a given village/cadaster eventually analyzing the credit worthiness of farmers

▪ Crop Outlook: Forecasting model generating yield and acreage estimates based on

forecasted and actual weather. Available 2-3 months prior to sowing period

Subscription based/pay-per use model

Identifies potential agri-distress hotspots for better planning &

mitigation

Ability to forecast yield & acreage 2-3 months prior to

farming season

Near real time view of weather, pest & disease attack event, soil

moisture, and crop losses


▪ All captured data is visible

on your portal database

▪ Allows easy navigation to

particular farms of interest

▪ Allows capturing of farm level

information by crop growth

stage

User Friendly Mobile App - Farm Management


Used Case Study

Satellite Based Acreage, Yield Estimation and

Crop Health Monitoring


Used Case Study

Berchha

Village with

village

boundaries

Nagda

Problem Statement: Stakeholders required details w.r.t crop

classification, crop health, and yield & loss estimation


Overlay Owner Information

Number of Farmers 1229

Threshold yield for soybean crop’s claim payout (year 2016 - 2018)

921kg/ha

Farmer Name Hiralal

Khata No. 369

Crop Soybean

Area (Ha) 5.64


Crop Classification

Berchha

Soybean Acreage (ha) 761.55

Total Area (ha) 904.98


Crop Health Assessment and Area Level Yield Estimation

Crop Health assessment using

Vegetation Indices: The pigment in

plant leaves, chlorophyll, strongly

absorbs visible light (from 0.4 to 0.7

µm) for use in photosynthesis. The

cell structure of the leaves, on the

other hand, strongly reflects near-

infrared light (from 0.7 to 1.1 µm).

The more healthy leaves a plant has,

the more these wavelengths of light

are affected, respectively. Therefore,

usually a NDVI value approximately

0.4 or higher is considered as

healthy reflection of crop.

Estimated Yield for Berchha Village

Threshold Yield

921 Kg/ha 20% below normal

Predicted Yield

411 Kg/ha 62% belownormal

(Berchha Village, Nagda, Ujjain)


Cadastral Level Yield Loss to Soybean Crop

(Berchha Village,

Nagda, Ujjain)


Overlay Insured Acreage

Loanee Farmers 829

Non-Loanee Farmers 400

Loanee Farmers withCorrect Policy

538

Loanee Farmer with Incorrect Policy

292

Loanee Acreage for Correct Policy (ha)

323.2

Non-Loanee Acreage 438.3


Farmer Credit Worthiness

Crop health condition map which has been

generated using 2019 Vegetation Condition Index

(VCI) values for the cotton classified areas.

If credit score

≤ 6 = A (Good performing cadastre);

> 6 to 9 = B (moderate performing cadastre);

> 9 = C (poor performing cadastre)

Cotton Crop Health Map Credit Rating Map


Remote Sensing Based Crop Health Assessment


Crop Outlook: Paddy & Maize Crops’ Expected Production

Forecasting model generating yield and acreage estimates based on forecasted & actual weather.

Reports at country, state or district level, readily available on the portal for immediate download.

Identification of all potential agri-distress hotspots in India for better planning and mitigation. Available

2-3 months prior to sowing period


Crop Outlook: Sample Yield Dip Hotspots


Expected Crop Acreage, Yield, and Production for Major Crops

Crop

Acreage deviation

w.r.t. normal

acreage (%)

Yield deviation w.r.t.

normal yield (%)

Production

deviation w.r.t.

normal production

(%)

Arhar (Pigeonpea)-Rainfed 2.20% -2.94% -0.80%

Bajra (Pearl millet)-Rainfed -10.80% -1.65% -12.28%

Barley-Rainfed -12.70% -0.53% -13.16%

Chilly-Rainfed 0.98% 0.00% 0.98%

Cotton-Rainfed -6.03% -6.02% -11.69%

Groundnut-Rainfed -6.41% -11.01% -16.72%

Jowar (Sorghum)-Rainfed -7.33% -9.18% -15.84%

Jute-Rainfed -3.98% -15.01% -18.39%

Maize-Rainfed -3.00% -11.69% -14.34%

Moong (Green gram)-Rainfed -6.30% -6.18% -12.09%

Paddy-Rainfed -6.61% -9.04% -15.06%

Ragi (Finger millet)-Rainfed -5.06% -5.92% -10.68%

Sesame (Til)-Rainfed -2.61% -8.20% -10.60%

Soybean-Rainfed -6.60% -6.94% -13.08%

Sugarcane-Rainfed -6.12% -6.98% -12.67%

Sunflower-Rainfed -4.10% -6.57% -10.40%

Urad (Black gram)-Rainfed -7.78% -5.29% -12.65%


Global Experience

PROJECT DESCRIPTION PROJECT LOCATION CLIENTS

Satellite data interpretation of Open Burning areas Philippines World Bank

Forestry and Livelihood Development with special reference to Climate Change Adaptation Strategies

Cambodia FAO, Cambodia

Oil Palm Suitability Analysis Cameroon , West Africa Siva Group

Consultancy Services to create a Geo-referenced database on Hotspots in Irrigation Catchments

Malawi Ministry of Agriculture, Government of Malawi and Techno-Brain

Crop Acreage Mapping - 2018-19India, Pakistan, China, Philippines, Thailand, Vietnam

Monsanto Ltd.

Soil Quality studies in Mwaladzi, Mozambique Tete, Mozambique Rio Tinto

Forest Mapping and Biomass Estimation Russia Indufor Oy, Finland

Agriculture Parcel Mapping and Analysis using RS & GIS technique for Quassim Region, KSA

Kingdom of Saudi Arabia Causeway (Ministry of Agriculture, Saudi Arabia)

Agriculture Change Dynamics studies and Geospatial consultancy for development

Saudi Arabia Causeway (Ministry of Agriculture, Saudi Arabia)

Chickpea Acreage and Production estimation using Remote Sensing and GIS technique

Madhya Pradesh HAKAN AGRO DMCC

Crop Insurance Product development for Tea, Rubber, Wheat & Potato

Uttar Pradesh, Kerala World Bank & AIC India

CCE yield scoring using RS India World Bank - AIC


Regional Experience

PROJECT DESCRIPTION PROJECT LOCATION CLIENTS

Remote Sensing based FCV Tobacco Crop Acreage Estimation, Rabi

Andhra Pradesh, India ITC Agri division

Crop Mapping & Yield Estimation of Safflower Maharashtra Marico- BTS

Subscription for Crop Information for Mustard, Maize and Lentil

Madhya Pradesh, Rajasthan, Uttar Pradesh, Haryana &Bihar

Cargill, Monsanto India Limited, Jawaharlal & Sons

Satellite based Soybean & Guar acreage, production & yield estimation in Kharif

Rajasthan, MP, Maharashtra, Punjab & Haryana

Ruchi Soya Industries

Satellite based Corn mapping on PAN India India Dupont / Pioneer

Remote Sensing based Corn Crop Acreage Estimation in Kharif 2015

Karnataka, Madhya Pradesh Monsanto India

Groundnut crop mapping, Acreage & Production Estimation in selected districts for Kharif- 2014

Gujarat IOPEPC

Satellite and Field Based Soybean Crop Acreage, Yield and Production Estimation for Kharif 2015, in India

Madhya Pradesh, Maharashtra and Rajasthan

The Soybean Processors Association of India (SOPA)

Satellite based Corn and Rice mapping Rajasthan, MP, Punjab & Haryana

DSCL - Shriram Fertilisers & Chemicals (SFC)

Crop Acreage, Yield Estimation, Crop Health Monitoring and Crop cutting experiment for Paddy, Pigeonpea, Cotton (ongoing)

30 districts across IndiaMNCFC, Ministry of Agriculture, Govt. of India


Key Clients


Thank You!For any query, please drop us a mail at

[email protected]

mailto:[email protected]

Documents

RMSI Cropalytics