A Transfer Learning Approach to Classify InsectDiversity Based on Explainable AI

This paper suggests an approach to insect identification using transfer learning combined with an explainable artificial intelligence (XAI) technique. Thisstudy represents the importance of incorporating XAI to ensure the reliabilityand trustworthiness of automated insect identification systems. The proposedresearch influences the expanding body of knowledge on advanced AI techniquesfor biological classification, paving the way for future innovations in entomology, agriculture, and ecological monitoring. Transparency and interoperabilitymade possible by the integration of XAI enable a thorough comprehension ofthe decision-making process underlying the model’s predictions. The gradient-weighted class activation mapping, or Grad-CAM, approach is what we employ. Establishing transparency is crucial in fostering confidence in automated systemsand guaranteeing their pragmatic implementation in real-life situations. We proposed the ResNet152v2 model achieving a classification accuracy of 96% on acomprehensive dataset of 4,509 insect images, spanning 9 distinct classes. Thisapproach boosts model performance and reduces the dependency on extensivelabeled datasets.