Enhancing Tea Leaf Disease Recognition with Attention Mechanisms and Grad-CAM Visualization
This work addresses the need for efficient disease identification in tea production to prevent economic losses, but it is incremental as it applies existing methods to a new dataset.
The study tackled the problem of automating tea leaf disease recognition to aid farmers, achieving an accuracy of 85.68% with an ensemble model that incorporated attention mechanisms and Grad-CAM visualization.
Tea is among the most widely consumed drinks globally. Tea production is a key industry for many countries. One of the main challenges in tea harvesting is tea leaf diseases. If the spread of tea leaf diseases is not stopped in time, it can lead to massive economic losses for farmers. Therefore, it is crucial to identify tea leaf diseases as soon as possible. Manually identifying tea leaf disease is an ineffective and time-consuming method, without any guarantee of success. Automating this process will improve both the efficiency and the success rate of identifying tea leaf diseases. The purpose of this study is to create an automated system that can classify different kinds of tea leaf diseases, allowing farmers to take action to minimize the damage. A novel dataset was developed specifically for this study. The dataset contains 5278 images across seven classes. The dataset was pre-processed prior to training the model. We deployed three pretrained models: DenseNet, Inception, and EfficientNet. EfficientNet was used only in the ensemble model. We utilized two different attention modules to improve model performance. The ensemble model achieved the highest accuracy of 85.68%. Explainable AI was introduced for better model interpretability.