Designing Weather Based Crop Insurance Payout Estimation Based on Agro-Meteorological Data using Machine Learning Techniques
1K.P.Mangani, Ph.D scholar, Department of Computer Science, Dr.N.G.P Arts and Science College, Coimbatore, India.
2Dr.R.Kousalya, Professor and Head, Department of Computer Applications, Dr.N.G.P Arts and Science College,, Coimbatore, India.
Manuscript received on 2 August 2019. | Revised Manuscript received on 9 August 2019. | Manuscript published on 30 September 2019. | PP: 2953-2960 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4806098319/2019©BEIESP | DOI: 10.35940/ijrte.C4806.098319
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Data mining techniques have been extensively used to mine up-to-date information from agricultural databases. In Agriculture, the Loss Assessment and Estimation in Crop insurance can be done on various factors like yield-based, crop-health based and weather-based variations. Weather-based variations are taken into account to design the insurance payout classifier model for the selected crop within the selected agricultural blocks of Tamilnadu. Then the weather attributes that undergone feature selection are given as input to the model with the rule-based classification algorithm implementing the neighboring approach with a sequential covering strategy named as CBKNN-PAYRULE which is statistically higher than other state-of-the-art rule-based classification algorithms. This model is proposed to classify the agricultural blocks based on the Area-wise Assessment of adverse temperature for the groundnut crop from their nearest neighbor. Then By combining the classified neighboring approach with the threshold factors the Rule-based classifier is done to generate the rules to estimate the insurance payout value as per policymakers for the selected agricultural blocks. Then decision-making techniques are applied to predict the insurance with the possibility of product basis risk, which covers the deviations in weather indices with the risk profile factors for the notified agricultural blocks for the specified crop. Thus the proposed technique can support the simultaneous prediction of the insurance payout to be paid in case of adverse weather factors of the selected crop for five districts with high accuracy and the correlation analysis of weather factors with the payout concerning to each district is also made. The Experimental results show that the proposed work enhances the accuracy in insurance payout prediction of the groundnut crop of the selected districts.
Keywords: Data Mining, Sequential Covering Algorithm, Agriculture, Crop Insurance Payout, Class-Based-KNN, Classification.
Scope of the Article: Classification.