Detection of plant diseases in tomato leaves : With focus on providing explainability and evaluating user trust

Sammanfattning: The transmission of diseases from unhealthy to healthy plants is one of the most disastrous threats to the agriculture industry. Diseases transferred spread like wild fire and have the potential to infest the whole farm if not detected early. Plant disease detection methods aid in identifying infected plants in their very early stages and also help the user in scaling the identification of plant diseases to a variety of plants in a cost-effective manner. The aim of this thesis is to implement two different machine learning models, namely, Convolution Neural Networks (CNN) and K-nearest Neighbors (KNN) for the application of plant disease detection in tomato leaves.The two machine learning models were evaluated on four different metrics in order to find the best performing model among the two. The four different metrics were, Accuracy, Precision, Recall and F1-Score. Other than identifying the diseases using the aforementioned machine learning models, this study also focused on providing explainability to the predictions made by the respective models using the Explainable Artificial Intelligence technique, Local Interpretable Model-agnostic Explanations (LIME). In vein of collecting domain specific expertise, a user study was implemented in which the user trust of the AI and XAI models were evaluated and feedback from farmers were collected in order to provide recommendations for future research.The results on implementing the machine learning models showed that the CNN model performed better than the KNN model in all of the four evaluation metrics and the results from the user study signify that the farmers do not trust the AI and XAI models, however, the user study through the feedback collected from the farmers helps identify areas in which the trust of the farmers can be grown and strengthened.