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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Trung, Hieu Nguyen
FPT University, Can Tho 94000, Vietnam
Computer Science & IT Electrical & Electronics Engineering

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Advanced Classification of Agricultural Plant Insects Using Deep Learning and Explainability Vo, Hoang-Tu; Thien, Nhon Nguyen; Mui, Kheo Chau; Tien, Phuc Pham; Le, Huan Lam; Phuc, Vuong Nguyen; Trung, Hieu Nguyen; Tan, Phuong Lam
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 2: June 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i2.6419

Abstract

This paper investigates the effectiveness of six pre-trained deep learning models to classify images of agricultural plant insects. We utilized the BAUInsectv2 dataset, which includes images from nine classes. Aphids, Armyworm, Beetle, Bollworm, Grasshopper, Mites, Mosquito, Sawfly, and Stem borer. The models, namely Xception, MobileNetV2, ResNet50, EfficientNetV2B3, ResNet101, and DenseNet121, are fine-tuned by transfer learning from ImageNet. This approach significantly reduces training time while improving classification accuracy. Our experiments reveal that each model reliably distinguishes between insect species even when faced with varying lighting conditions and diverse viewpoints. To further clarify how these models make predictions, we employ Gradient-weighted Class Activation Mapping (Grad-CAM) to highlight critical regions in the images. The results demonstrate that each model focuses on unique biological features and offers clear explanations for its decisions. The research results contribute to demonstrating the potential of pre-trained deep learning architectures for agricultural monitoring and pest management, paving the way for promising future applications.
Co-Authors Le, Huan Lam Mui, Kheo Chau Phuc, Vuong Nguyen Tan, Phuong Lam Thien, Nhon Nguyen Tien, Phuc Pham Vo, Hoang-Tu