An efficient VGG16-based deep learning model for automated potato pest detection
Nibedita Deb, Tawfikur Rahman
Abstract
The timely and accurate detection of potato pests is crucial for mitigating crop losses and supporting sustainable agriculture. This study presents an efficient deep learning model based on the VGG16 architecture for the automated classification of common potato leaf pests, including aphids, Colorado potato beetles, leafminers, and ladybird beetles. A curated dataset of 3,000 annotated images, comprising both pest-infected and healthy leaves, was compiled from authentic field images directly from rural agricultural areas. To enhance generalization, the model was fine-tuned using transfer learning and optimized with data augmentation techniques, including rotation, flipping, zooming, and contrast adjustment. Experimental results demonstrate that the proposed VGG16-based model achieves superior performance across accuracy (96.3%), precision (95.8%), recall (96.1%), and F1-score (95.9%) metrics, outperforming baseline architectures including ResNet50, InceptionV3, MobileNetV2, and EfficientNetB0. Additional analyses, such as confusion matrices, ROC-AUC curves, and class-wise evaluations, confirm the model's robustness in real-world scenarios. With an average inference time of 45 ms per image on a Kaggle environment (e.g., Tesla P100 or T4 GPU) and successful deployment potential on edge devices, the proposed model offers a scalable, lightweight and practical solution for real-time pest monitoring in precision agriculture. Future work will focus on expanding pest diversity, improving model interpretability, and integrating the system into IoT-enabled smart farming frameworks.