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This research introduces a lightweight dual-attention network, LDAMNet, for identifying tomato leaf diseases using machine vision techniques. The model employs a dual attention mechanism to enhance feature extraction and utilizes a Robust Cross-Entropy loss function to mitigate the impact of noisy labels. Experimental results demonstrate an impressive average recognition accuracy of 98.71%, showcasing the model

Author

Vijaya Bathini

Language

EN