Predictive modelling of yellow stem borer population in rice using light trap: A comparative study of MLP and LSTM networks

Abstract

The yellow stem borer (YSB), Scirpophaga incertulas (Walker), is a major insect pest that significantly damages rice crop. This study investigates methods to predict YSB populations in rice fields, aiming to develop an early warning system. Traditionally, rice farmers rely on light traps to monitor YSB presence. However, this study goes beyond this approach by combining light-trap data with weather information (temperature, humidity, rainfall) and utilizing powerful artificial intelligence (AI) techniques to forecast future YSB populations. Two AI methods, multilayer perceptron (MLP) and long short-term memory (LSTM), were employed to estimate YSB populations and assess their performance. The results revealed that the LSTM model outperformed the MLP model based on statistical metrics like RMSE, MAE, and R² values. Utilizing LSTM model with historical data, stakeholders in the Eastern Coastal Plains and Hills agro-climatic zone of India can gain a significant advantage in predicting YSB populations well in advance. This early warning system can alert stakeholders of potential YSB outbreaks, allowing them to take timely management actions and protect their rice crops from substantial yield losses.