Abstract
Weather-based simulation models were developed in the present study to characterise late blight of potato in the Lower Gangetic Plains using AI-based machine learning and multiple linear regression approaches. Weather variables considered in this study were, daily maximum temperature, minimum temperature, rainfall, morning relative humidity, evening relative humidity, wind speed, and solar radiation, observed during the potato crop growing period, along with the disease parameter — crop age at first appearance of disease (CAFAD) — from 2006 to 2020 (15 years). A total of 56 simple and weighted weather-based indices were developed which served as inputs for the model development and validation. Simulation models were developed using machine learning approaches like LASSO, SVM, and multiple linear regression technique SMLR, along with two hybrid models like LASSO-SVM and SMLR-SVM under early, normal, late, and all (pooled data) planting conditions in lower Gangetic plains. Out of all the AI-based machine learning and multiple linear regression models developed, based on the overall standardised Ranking Performance Index (sRPI), the SMLR model was considered to be the best to simulate crop age at first appearance of the disease under early and normal planting conditions, whereas SVM and LASSO were the best under late and all planting conditions, respectively. The developed models can be used in decision support systems to predict potato late blight and can also be extended to develop similar models for other locations.
