Plant Leaf Disease Detection using Transfer Learning and Explainable AI

Abstract

Among the major occupational sectors around the world, agriculture has the highest level of involvement. Every year, this sector faces a substantial loss in production and profit due to a large number of diseases in crops and plants. If those diseases are not detected early and taken measures for prevention, it can bring about a devastating result that can financially burden agriculture personnel. Traditional methods of detecting diseases in plants and crops offer high accuracy. However, the procedure is time-consuming, which might be insidious in most cases. Crop diseases need to be detected and cured as soon as possible as most diseases are highly contagious among crops and plants. In this paper, we have used the transfer learning approach with three pre-trained models: EfficientNetV2L, MobileNetV2, and ResNet152V2, to detect various plant diseases. We have proposed a framework to detect 38 types of leaf diseases in 14 different plants, compared the three pre-trained models based on various quantitative performance evaluation parameters, and demonstrated that EfficientNetV2L performed best with 99.63% accuracy. In the end, Explainable Artificial Intelligence (XAI) technique: LIME has been implemented in our model to understand the insight view of the model EfficeintNetV2L's for such prediction. It is used to make our model's predictions more reliable and gives a clear explanation about the reason of such decision.