Tea is among the most widely consumed drinks globally. Tea production is a key industry for many countries. One of the main challenges of tree harvest is tea leaf diseases. If the spread tea leaf diseases are not stopped in time, it can lead to massive economic losses for the farmers. So, it’s crucial to identify tea disease as soon as possible. Manually identifying tea leaf disease is an ineffective and time-consuming method, without any guarantee for 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 kind of tea leave diseases allowing the farmers to take actions to minimize the damage. A novel dataset was developed specifically for the purposes of this study. The dataset contained 5278 images for seven class. The dataset was pre-processed prior training the model. We deployed three pretrained model, DenseNet, Inception and EfficientNet. EfficientNet was only used 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.