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

• Published in: Annals of applied biology 2024-09, Vol.185 (2), p.255-263
• Main Authors: Bapatla, Kiran Gandhi, Gadratagi, Basana Gowda, Patil, Naveenkumar B., Govindharaj, Guru‐Pirasanna Pandi, Thalluri, Lakshmi Narayana, Panda, Bipin Bihari
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Borers; Cereal crops; Coastal management; Coastal plains; Comparative studies; comparative study; Crop damage; Crop yield; data‐driven; Early warning systems; humidity; India; insect pests; Insects; Light traps; long short‐term memory; Meteorological data; Multilayer perceptrons; neural network; neural networks; Pest outbreaks; Plant protection; Population studies; Populations; Prediction models; rain; Rainfall; Rice; Rice fields; Scirpophaga incertulas; stakeholders; Statistical models; temperature; Temperature perception; time series
• ISSN: 0003-4746; 1744-7348
• DOI: 10.1111/aab.12927

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. The deep learning models were developed using daily light‐trap catches of yellow stem borer along with climate variables in rice crop, and these models may be useful to develop effective monitoring and management tools.