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A Lightweight Deep Learning Model for Tea Leaf Disease Identification

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The proposed model achieves accuracies of 99.49% and 90.73% for disease identification.
Significant reductions in computational costs and model size are reported, making it suitable for edge devices.
The method incorporates a modified EfficientNetB0 with the ECA module for improved efficiency.
Two datasets were utilized: the Tea_Leaf_Disease dataset and the teaLeafBD dataset, containing multiple disease categories.

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This approach utilizes deep learning to enhance identification accuracy of tea leaf diseases, suggesting practical applications in agriculture.

Lien et al. (2025) studied this question.

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This approach utilizes deep learning to enhance identification accuracy of tea leaf diseases, suggesting practical applications in agriculture.