ExplainableAI with Transfer Learning for Crop Disease Assessment Using Thermal Imaging

The process of maintaining crop to preserve its health naturally is responsible and challenging task in this era of chemical pesticides/insecticides. So, crop management before it is affected by any disease can be a crucial step ahead to avoid the overuse of chemicals. This current work deals with the application of plant disease prediction from thermal images. For this purpose, the popular transfer learning model, EfficientNetB0 utilized for multi-label classification problem. Normal image classification causes more errors than thermal image classification because of blurry images, no clear visuals of the leaves, temperature variations that can't be determined, etc. So EfficientNetB0 model is used which is more balanced, accurate, and efficient. This EfficientNetB0 model combines XAI for a better understanding of the model and gives a higher rate of accuracy than other traditional models. This study highlights the combination of a deep learning model and thermal imaging technique to build a strong, non-invasive mechanism for crop disease management.