Timely Detection of Stem Borer Pest Infestation through Convolutional Neural Network

The Yellow Stem Borer (YSB), Scirpophaga incertulas (Walker), is an important pest of rice throughout tropical South and Southeast Asia. The highest incidence of this pest is primarily observed in tropical lowland rice fields and deep-water rice cultivation. The yield loss caused by the YSB is estimated to be 20% in early-planted rice crops and 80% in late-planted crops. In this paper, we developed a method to detect and classify the forms of YSB using a Convolutional Neural Network (CNN) and then model the infestation migration patterns of YSB in several rice-growing regions by using a CNN learning model. A dedicated CNN architecture is designed, and optimized for its ability to extract features and discern spatial hierarchies indicative of pest presence. Transfer learning techniques, utilizing pre-trained models, enhance the model's capability to recognize subtle patterns associated with pest infestations. The dataset is carefully annotated and augmented to ensure robust model training, with an emphasis on real-world variability. These models can help detect, classify, and model the infestations of other Agricultural pests, improving food security for rice.