A transfer learning approach to classify insect diversity based on explainable AI

Abstract Insect identification is crucial for agriculture, entomology, and ecological monitoring, where accurate pest detection can avoid crop damage and reduce pesticide use. To assure model transparency and dependability, this work suggests an improved method for automated insect categorization that combines explainable artificial intelligence (XAI) techniques with transfer learning. The main goal is to create a high-accuracy, easily deployable classification system with excellent interpretability by utilizing the ResNet152v2 architecture. Nine different classes of insects totaling 4509 photos were gathered and pre-processed for noise reduction, resolution standardization, and data normalization. The ResNet152v2 model was trained, and Grad-CAM (gradient-weighted class activation mapping) was applied to illustrate significant characteristics driving model decisions. With a 96% classification accuracy, the model proved useful in practical applications, minimizing the need for big labeled datasets while preserving transparency. Using this model in agricultural contexts can help farmers protect crops from pests, use less pesticides, and improve farmland fertility, among other practical uses. This system, which uses a web-based application, is accessible, inexpensive, and simple to use. It provides farmers with timely information so they may take preventive measures against pest infestations. This work contributes significantly to the field by providing a scalable and trustworthy solution for real-time insect identification, laying the groundwork for future AI-driven innovations in sustainable agriculture and ecological monitoring.