Optimising Insurance Payouts and Estimation of Crop Yield to Support Agricultural Decision Making with AI &amp; ML
Creators: name: Kunal Prasad
name: Kumar, Krishna
name: Aravind S
name: Shabarinath Nair
name: Dhruv Puri
Publisher: name: Gates Open Research
Year: 2020

description: The project is part of the nationwide crop insurance scheme for which the Indian states have to carry out an assessment called Crop Cutting Experiment (CCE) in every Gram Panchayat (GP). The government of India launched the Pradhan Mantri Fasal Bima Yojana (PMFBY) in 2016 along with Mahalanobis National Crop Forecast Centre (MNCFC) in the motivation to bring down the operational overheads and improve the efficiency of the crop insurance system by precisely estimating yield at Insurance Unit (IU) level. Cropin was one of the companies selected to estimate the yield at Gram Panchayat level using multiple technologies and globally accepted methodologies. In this project, the objective is to demonstrate a methodology to estimate the yield at Gram panchayat with Cropin's proprietary yield model with field collected CCE data. The CCE points should be decided based on a smart sampling procedure with the help of required or influential remote sensing and weather perils. In this research, the objectives were to estimate crop yield using another known energy balance model named Mapped EvapoTranspiration at high resolution with Internalized Calibration (METRIC). This model is less dependent on climatic measurements to compute actual evapotranspiration. The long-term goal is to replace the CCEs or optimize the CCEs by this methodology for GP level yield assessment.<br>Crop insurance in India is largely parametric in nature with yield index as the basis with the village as the insurance unit for the major crops under Pradhan Mantri Minister Fasal Bima Yojna (PMFBY). The village has been selected as an insurance unit to account for the high intra-insurance unit yield variabilities in the earlier insurance schemes. In this scheme, the loss assessment is done using sample crop cutting experiments (CCEs). <br>In this project, the objective is to demonstrate a methodology to estimate the yield at Gram panchayat with CropIn proprietary yield model with field collected CCE data. The CCE point should be decided based on a smart sampling procedure with the help of required or influential remote sensing and weather perils In this research, the objectives were to estimate crop yield using another known energy balance model named Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC). This model is less dependent on climatic measurements to compute actual evapotranspiration. The long-term goal is to replace the CCEs or optimize the CCEs by this methodology for GP level yield assessment.<br>Currently, loss assessment through CCEs is the only method being practiced. With the increased coverage, conducting and monitoring the exorbitant number of CCEs over a short period is a herculean task. Over the years, CCEs have increased manifolds. In this scenario, the quality and precision of the loss assessment can be seriously compromised. Besides, a huge number of CCEs adds a substantial burden both in terms of monetary and human resources. Most importantly, there is early evidence that these increased samples are not likely to result in improved loss assessments and appropriate pay-outs (Dadhwal et al. 2003; Tripathy et al. 2013; Ray et al. 1999; Bandyopadhyay et al. 2014). In this pilot study, CropIn leverages the technology folded with Remote Sensing, There are a number of methods available to estimate the yield including weather-based estimation, crop simulation studies, vegetation indices from remote sensing data, integrated methodology with help of high spatial resolution data derived from remote sensing products. Crop simulation models include different parameters such as daily weather parameters, crop management factors, and genetic coefficients to predict the yield at a larger scale. Crop models can simulate yields for a wide range of environments if the input information is sufficient and accurate (Asseng et al., 2013). Evaluating crop performance under prescribed scenarios of climate, soil, and management constitutes the most typical application of crop models. However, their application for assessing crop yield under actual field growing conditions is often restricted by input data availability (de Wit and van Diepen, 2007; Dente et al., 2008; Ma et al., 2012; Mignolet et al., 2007; Zhao et al., 2013). <br>The process involved the following steps 1. Generating crop maps based on prediction using crop detection models trained on Cropin’s crop signature database 2. Creating a combined layer using Normalized Polarization ratio (NPOL), Evapotranspiration (ETc,adj) and relative vegetation metric to cluster the crop layer based on potential health/yield characteristics 3. These points are further optimized at the Gram Panchayat Level based on homogeneity. At this stage the number of points in a GP is kept at the minimum proposed as per government guidelines. 4. Energy balance model assimilated with remote sensing features includes LST, Albedo, LAI etc. and crop specific dynamic coefficients were used to predict the yield data and finally using the ground collected CCE yield values the comparison has been performed to validate the yield predicted at different strata are matching with actual yield with the help of statistical tests.