© Niruthi LTD.

Data Analytics for Protecting Rural Livelihoods

Products

Crop Monitoring and Forecasting

Crop Yield andDamageAssessment

Location Specific Current and Historical Climate

Expert Alerts andAdvisories

3

TechnologySense

Satellites | Drones

Weather Stn | Mobiles

IoT

Predict

Machine Learning - photo-to-feature

AI - agro-advisories

Crop modeling (nowcasts, forecasts)

Data assimilation (point-to-space)

Data mining for anomalies

Cloud Compute

Scalable Data Stores & fast query

Elastic compute & rapid storage

Cost optimized

Collaborate

Weather advisories

Crop advisories

Sampling schemes

Crop statistics

Communicate

Viz | Newspaper | TV

IAV| web | APIs

An end-to-end intelligent information system to serve the

many needs of insurance providers, agencies and

farmers.

Technology

4

Maharashtra Pune District Indapur Taluk/Circles Indapur Taluk/Villages

Agro-weather Advisories Crop Insurance

Tens of farms/village

Crop yields

Phase I (Sponsored by SwissRe)

(Niruthi/AIC/CRIDA/GoMH)

Objective:

• To create village-level historical

climate and crop yields to assess

basis risk for insurance

What was done:

• Implemented TOPS, integrating

ground, satellite observations

Outcome:

• Created historical daily climate data

for 40,000 villages covering 1981-2013

• Created crop yield models for Bajra,

Cotton, Gram, Jowar and Soybean

Phase II (PPP/IAD)

(GoMH/Niruthi/CRIDA/SwissRe/AIC)

Objective:

• To reduce the number of CCEs

through smart sampling and geo-

spatial data

What was done:

• Conducted over 12,000 CCEs

• Created dynamic sampling

schemes near harvest season

Outcome:

• Smart sampling can save 40-70% of

resources and stay within

10%accuracy at the Circle level

Phase III (PPP/IAD)

(GoMH/Niruthi/CRIDA/SwissRe/AIC)

Objective:

• To test the ability to settle claims at

village-level

What was done:

• Deployed CropSnap (mobile

technology) with smart sampling to

assess yields in 108 villages

Outcome:

• CropSnap yields are within 15% in

Jowar, Bajra, Gram, and 22% in

Soybean

• Costs less than 25% and meets the

village-level insurance guidelines

Our experience spns the entire range

Experience: High resolution climate and crop yields for insurance in MH

Data – Model Assimilation for Crop Yield Forecasts/Estimates Forecasting (mid-season)

Method:

• Based on modeled

photosynthesis integrating

satellite data, ground data,

models

Outcome:

• Categorical yields (normal,

below, above)

Final Estimation

Method:

• Based on modeled

photosynthesis integrating

satellite data, models and

CCE data from the State

using smart sampling

Outcome

• Probabilistic yield surface

at field scale, can be

aggregated to farm, village,

mandal or district

Probabilistic yield surface

Sm

art

Sam

plin

g

Crop Yield Recognition System (CYRS), Virtual Crop Cutting Experiments

Good results but cumbersome to deploy,

difficult to train, significant time-lag, and

non-scalable

Automated segmentation results for wheat crop

Field deployment in Soybean, 2014 –2015

Mobile App for field-level data collectionAllows for rapidly capturing the spatial variability that traditional CCEs may not

CropSnap allows users to record a variety of information about crops grown, crop condition, canopy density and expected yields.

Information is automatically uploaded onto the cloud where it is integrated with satellite data to create maps of farm-village-regional assessments.

Soybean (40 villages)y = 0.79x + 0.81

R² = 0.59RMSE=1.7 (22%)

0

2

4

6

8

10

12

14

16

0 2 4 6 8 10 12 14 16

Me

asu

red

Yie

ld, q

/ha

CropSnap Yield, q/ha

Low Med High

<6 6 - 12 >12

Village 1 21 6 3

Village 2 10 19 1

Village 3 3 10 17

Minimum 30 locations per villageExample distribution of yields

High YieldMediumYield Low Yield

Scale-invariant deep learning algorithm trained on 2060 samples with coincident CCE and photos

Input = CropSnap photosOutput = low, med, high

BAJRA (14 villages)y = 1.0x - 0.092

R² = 0.75RMSE = 0.52 (14%)

0

1

2

3

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6

0 1 2 3 4 5 6

Me

asu

red

Yie

ld, q

/ha

CropSnap Yield, q/ha

Scale-invariant deep learning algorithm trained on 1666 samples with coincident CCE and photos

Measured yields are derived from 4 CCEs per village

Summary

Niruthi technology provides a transparent, scalable, cost-effective approach needed to expand access to crop insurance.

CropSnap Mobile app pictures translated to crop yields save time, money and improve accuracy through large number of samples

Innovations in mobile computing may allow on-board estimation of yield categories, reducing the need for bandwidth and connectivity.

Further training may allow additional yield categories, ultimately actual yields.

Contact us

Contact Us

Mr. Mallikarjun Kukunurimkukunuri@niruthi.comwww.niruthi.com402 Man Bhum Jade TowersSomajiguda, HyderabadTelangana, 500 082