A Lightweight Deep Learning Model for Tea Leaf Disease Identification

Tea is a globally important economic crop, and the ability to quickly and accurately identify tea leaf diseases can significantly improve both the yield and quality of tea production. With advances in deep learning, many recent studies have demonstrated that convolutional neural networks are both feasible and effective for identifying tea leaf diseases. In this paper, we propose a modified EfficientNetB0 lightweight convolutional neural network, enhanced with the ECA module, to reliably identify various tea leaf diseases. We used two tea leaf disease datasets from the Kaggle platform: the Tea_Leaf_Disease dataset, which contains six categories, and the teaLeafBD dataset, which includes seven categories. Experimental results show that our method substantially reduces computational costs, the number of parameters, and overall model size. Additionally, it achieves accuracies of 99.49% and 90.73% on these widely used datasets, making it highly suitable for practical deployment on resource-constrained edge devices.