The Government of India launched Pradhan Mantri Fasal Bima Yojana (PMFBY), a nationwide crop insurance programme in 2016. The programme that provides a comprehensive insurance cover against the failure of the crops was to help stabilise the income of the farmers. As per the scheme, the states should carry out a minimum of four crop-wise Crop Cutting Experiments (an assessment method to estimate the yield of a crop during a cultivation cycle)in every gram panchayat. Traditionally, the government relied on random survey methods to estimate the crop yields of a given location. However, the method was very cumbersome. Thus the government chose to use artificial intelligence and machine learning technologies along with remote sensing imageries, and modelling tools to reduce the time lag for settling of claims of the farmers.

The challenge

There are 2.5 lakh gram panchayats in India. It was very challenging to conduct reliable and accurate CCE in the country at scale within a short harvesting window. Data collection was difficult. Thus the government needed to have a robust system for assessing crop loss and fool-proofing the insurance claim settlement process.

To streamline the CCE process and make it more accurate and scalable, the Central Government partnered with CropIn Technology Solutions, a Bangalore based AI company. 

Harnessing AI, CropIn provided technical support to conduct reliable, accurate, and large scale CCE within a short harvesting window and limited manpower. “In the last two years, we have done substantial work to optimise the CCE process for cotton, paddy, maise and other crops in states such as Maharashtra, Madhya Pradesh, and Karnataka. AI-based tools helped these states to significantly reduce the processing time for settling insurance claims,” says Jitesh Shah, Chief Revenue Officer, CropIn.

The AI solution 

 The central government decided to leverage CropIn’s SmartFarm and SmartRisk solutions to enable accurate and efficient execution of the CCE and consequently ensuring timely clearance of the insurance claims. The digital farm management solutions brought all the stakeholders- farmers, government agencies, insurance companies, and financial institutions on a unified platform enabling better administration, and bringing transparency in the process by providing real-time information and monitoring.

SmartFarm captures the precise location and size of the farm, details of farmers and the crop details right from the pre-harvest stage. The solution also enables accountability, efficiency, and transparency in farms by geo-tagging. This ensures that the field data is accurate, enabling authorities to easily use relevant data at the appropriate time. “SmartRisk makes use of both ground-level data and satellite imagery to identify the plots that are apt for these experiments. A dedicated and highly skilled data science team analyses millions of data points and runs them through numerous criteria to zero in on-farm plots that will provide the most accurate sample for the region. With the help of this data, authorities could easily identify the right plots that were included in the study, removing all ambiguity from the process of selection,” says Shah. Apart from this SmartRisk helped in estimating crop health, yield proxy of a given crop in a district, and acreage.  

“In 2019, we concluded the project,” Shah says. The AI solutions have helped significantly reduce the processing time for settling insurance claims. In the next phase, the company is planning to do a countrywide programme and cover major districts in India where agriculture challenges are present. “We are looking to partner with MNCFC and Government of India in their projects focusing on improving the livelihood of the farmers. By utilising the project-based learnings, we can make our solutions more efficient and reduce the cost incurred on manual CCE,” sums up Shah.