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Until now, we have seen that the yield estimates from Crop Cutting Experiments (CCE) act as a base for government agencies and insurance companies to disburse insurance claims in case of crop failures. CCE refers to an assessment method employed by the State/UT governments in India to estimate the yield of a crop in a district or sub-district during a given cultivation cycle. These yield measurements are time-consuming, cost-prohibitive, require trained manpower, and are prone to moral hazards.

Growing concerns on the quality of yield data have reduced the risk assessment ability of the insurance scheme, leading to a rise in premiums, delay in claims settlement etc. The National Remote Sensing Centre (NRSC, ISRO) and Agriculture Insurance Company of India Limited (AICIL) developed an innovative crop risk assessment approach in the area-yield index insurance scheme. This assessment approach uses performance proxies rather than manual-yield measurements. The big earth data that data-centric technologies, such as satellites, weather stations and mobiles, produce are used to generate performance proxies.

This reduces dependence on the cumbersome and subjective manual-loss estimation process and enables speedy disbursal of farmers’ claims. Compared to the traditional CCE method of using random sampling, remote sensing technology provides much more accurate and timely estimation of crop yield loss.

The European Space Agency also helped us usher into the new area of agriculture remote sensing by providing an unprecedented amount of free data at 10-meter resolution, once every 5–12 days. Remote sensing also provides biophysical indices like Normalised Difference Vegetation Index (NDVI) that indicates crop greenness, Land Surface Wetness Index (LSWI) that indicates moisture content, Synthetic Aperture Data that shows crop structure, and Fraction of Absorbed Photosynthetically Active Radiation (FaPAR) for sunlight absorption. AI engines use these indicators to detect and quantify the impact of various calamities like droughts, floods, inundation, lodging, pests, diseases, etc., on crop health.

Remote sensing technology was successfully tested by the AICIL along with the West Bengal Government under the Bangla Shashya Bima Scheme (BSB). This data analytics-driven platform provides insights on historical and current coverage, demographic characteristics, remote sensing parameters, crop losses and claims payable for every insurance unit, ensuring total transparency.

This technology, if deployed across the country, could replace crop-cutting experiments, save a lot of money, manual labour, and reduce the risk of reaching calamity-hit areas, overcome the biggest limitation of subjective manual-yield measurements, and effectively monitor the implementation of crop insurance schemes. This process, in turn, will ensure speedy claims disbursal to the farmers. Mining and forest sectors can also benefit tremendously from remote sensing technology.

We at the Research and Innovation Circle of Hyderabad are extremely delighted to support NRSC and AICL in the rollout of this technology for wider adoption across the country.