Explainable AI model for Cassava disease detection

Abstract

Agriculture is a crucial sector in Uganda’s economy, serving as the primary source of income for many Ugandans. However, plant diseases are a major issue in agriculture as they have an impact on both the quantity and quality of food produced. The death of the plants, a decline in farmer profits, and increased production costs during the control process are just a few of the detrimental effects of plant diseases. Cassava Leaf Diseases, which are caused by fungi, pose the greatest challenge of all the diseases, causing significant economic losses worldwide. Therefore, early detection and timely intervention are crucial for preventing the spread of the disease. Deep learning in computer vision has shown remarkable success in the performance of detection systems for plant diseases. However, due to the complexity and deeply nested structure of these models, these are still considered as black-box and explanations are not intuitive for human users. Many researchers have developed deep neural architectures for plant disease detection but have not provided classification explanations. To be used in practical applications, our model needs to explain why the model classified a given image. Explainable Artificial Intelligence (XAI) provides algorithms that can generate human-understandable explanations of AI decisions. In this paper, we summarize recent developments in XAI techniques, develop an Explainable AI Model for cassava disease detection, and most importantly, an explainable AI method named Gradient-weighted Class Activation Map- ping ++ (GradCAM++) is used to locate the disease and highlight the most important regions on the leaves contributing towards the classification.