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This paper presents a lightweight and interpretable hybrid deep learning model, Ghost-CBAM-LiteViT, for detecting potato leaf diseases, achieving over 99.67% accuracy with low computational intensity. The model integrates Ghost Convolutions, Convolutional Block Attention Modules (CBAM), and Lite Vision Transformer (LiteViT) to enhance feature extraction and classification efficiency. This approach aims to provide a scalable solution for real-time monitoring of potato diseases in resource-constrained agricul

Author

bapoj17700

Language

EN