Predictive Analysis of Occurrence of Thrips in Tomato Subject to Weather Parameters Using Machine Learning Techniques
Paper Details
Predictive Analysis of Occurrence of Thrips in Tomato Subject to Weather Parameters Using Machine Learning Techniques
Abstract
Thrips (Thripidae) on tomato (Solanum lycopersicum L.) at Rajendranagar, Andhra Pradesh, India is modelled based on field data sets generated during six kharif seasons [2011-18]. The weather variables considered are maximum & minimum temperature (MaxT & MinT) (0C), morning and evening humidity (RHM & RHE) (%), sunshine hours (SS) (hr/d), wind velocity (Wind) (km/hr), total rainfall (RF) (mm) and rainy days (RD). Thrips incidence was higher during 2012 and lowest in 2014. Correlation analyses significant positive influence of maximum temperature and negative influence of wind of one lags, RHM both current and one lags, rainfall one lag of negative influence on thrips. Machine learning techniques namelyAn empirical comparison of the above models [support vector regression (SVR), random forest (RF) and the other statistical models e.g., multiple linear regression (MLR), ridge regression (RR), least absolute shrinkage and selection operator (LASSO), and elastic net (EN)] is based on root mean square error (RMSE). It is observed that, for thrips, the RMSE values of RF and LASSO are less as compared to other competing models. Diebold-Mariano (D-M) test was applied for comparison of forecasting performance among the applied models. It is observed that, predictive accuracy of RF and LASSO is higher than that of other models.
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Satish Kumar Yadav, D. Pawar, Latika Yadav, Saurabh Tripathi (2024), Predictive Analysis of Occurrence of Thrips in Tomato Subject to Weather Parameters Using Machine Learning Techniques; IJB, V24, N5, May, P96-106
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