Plant Disease Detection Using Ensemble Technique and Explainable AI

Detecting plant diseases early and accurately is really important to prevent crops from failing and to make sure we have enough food. This paper looks at using a mix of computer programs that learn, along with "Explainable AI" (XAI) – which helps us understand how the programs make decisions – to get better at finding diseases and to make it clearer how the detection works. Powerful computer programs that are good at analyzing images (called CNNs) were trained on a set of pictures of leaves. Then, the predictions from those programs were combined using a method called "soft voting." This combined approach was very accurate (96.06%), and it did a better job than any of the individual programs. To deal with the fact that it's often hard to understand why these programs make the choices they do, Grad-CAM is used. Grad-CAM creates visuals that show which parts of the images the programs focused on when they were making their decisions. The results show that this combined method is effective at classifying plant diseases, and Grad-CAM is helpful in explaining how the programs reached their conclusions.