Open AccessMachine learning model for estimating agricultural crop insurance payout based on air temperature, rainfall, and relative humidity
Author(s) -
K.P. Mangani,
R. Kousalya
Publication year - 2020
Publication title -
agricultural science and technology
Language(s) - English
Resource type - Journals
eISSN - 1314-412X
pISSN - 1313-8820
DOI - 10.15547/ast.2020.02.028
Subject(s) - poisson regression , relative humidity , statistics , agriculture , linear regression , crop insurance , basis risk , mathematics , regression analysis , environmental science , econometrics , meteorology , geography , population , demography , archaeology , capital asset pricing model , sociology
. In Agriculture, the weather-based variations are deliberated to estimate the crop insurance payout. This research model includes linear regression technique (LR) for air temperature payout prediction and fuzzy based choquistic regression (FCR) technique for rainfall payout prediction of agricultural blocks. Then the combined indices of rainfall, relative humidity and air temperature are considered as input to the proposed model named fuzzy based Quasi Poisson Regression technique (FQPR) implementing the multi-indices evaluation function that performs the total payout prediction per hectare of the specified block. The deviations in weather indices determine the insurance payout value with the threshold parameter specified as per policy makers. Thus, the proposed techniques can support the prediction of the total insurance payout with additional weather parameters for the seasonal period of the selected crop for selected five districts with reduced error rate. The results show that the proposed work is appropriate for combining weather indices and predicting the total insurance payout of the groundnut crop of the selected districts.