Infection can consequently reduce both quality and yield, and causes major threats to tea production round the world. It is therefore sometime difficult to achieve fast, reliable, and precise identification of disease in tea plants and hence the need to embrace new methods of disease identification. To enable realisation of accurately understandable models for classification of the diseases in tea leaves, Explainable Artificial Intelligence (XAI) approaches are applied in this work. In order to train and test machine learning models, we collected a set of repos of high-resolution images of tea leaves affected by various diseases along with meta information. CNN models were trained with the help of our approach and adopting XAI tools as tools for explanation of predictions. From this study, the field of agricultural AI is benefitted from the illustration of how XAI might enhance disease management strategies in tea agriculture. The results demonstrate an accuracy of 87.85%, with precision, recall and F1-scores ranging between 0.78 and 0.95 across different classes.