An Explainable AI based Plant Disease Identification using a Two-Stage Detection-Classification Pipeline with YOLO and ECA-NFNet Framework

Plant disease looms over global food security as a significant threat. Despite this, accurately identifying diseases from images taken in real-world field conditions remains a major challenge. Standard classification models often fail in scenarios with complex backgrounds, variable lighting, and image noise characteristic of datasets like PlantDoc. To address this, this study proposes a robust two-stage detection-classification pipeline. The first stage uses a YOLOv11n object detector that was trained to find and separate leaf areas from their messy surroundings. It got a mean Average Precision (mAP@0.5) of 92.9%. In the second stage, these cropped leaf images are put into an ECA-NFNetL0 classification framework that uses a smart channel attention mechanism to find diseases in detail. On the hard-to-use PlantDoc dataset, our full pipeline gets a final classification accuracy of 78.5% and a weighted F1-score of 78.4%. This decoupled method, which separates localization from classification, makes the model much stronger and is a better way to diagnose plant diseases in the field.