Modeling and Predicting Rice Gall Midge Populations Using Climate Data and Machine Learning Techniques in Andhra Pradesh

Abstract

The Asian rice Gall midge (Orseolia oryzae (Wood-Mason) is a major insect pest affecting rice cultivation in South and Southeast Asia, leading to significant yield losses. Developing a reliable system for the timely prediction of this insect is crucial for effective pest management. In this study, Gall midge insect populations were recorded using solar light traps from three locations-Nellore, Maruteru, and Ragolu in Andhra Pradesh for the past 10 to 20 years.  Simultaneously, automatic weather stations close to these study sites recorded climatological parameters, including sunshine hours, rainfall, morning and evening relative humidity, maximum and minimum temperatures, and sunshine hours. Count time series models (Integer-valued Generalized Autoregressive Conditional Heteroscedastic (INGARCH)) and Machine learning models (Artificial Neural Network (ANN), Support Vector Regression (SVR) and Extreme Learning Machine(ELM)) were used to analyze weekly cumulative Gall midge populations and weekly averages of climatological data. To improve prediction accuracy, hybrid models (INGARCH-ANN, INGARCH-SVR, and INGARCH-ELM) were also created. Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) were used to assess the model's performance. The results indicated that the hybrid models, particularly INGARCH-SVR and INGARCH-ELM, outperformed standalone models in predicting Gall midge populations. The findings highlight the potential of integrating time series modeling with machine learning techniques to improve pest forecasting and aid in proactive, site-specific pest management strategies, thereby minimizing economic losses and ensuring sustainable rice production.