Plant diseases significantly impact agricultural productivity, and early detection is critical for preventing widespread crop damage. This chapter presents a Potato Leaf diseases Detection and classification model using an enhanced YOLO, namely, PLD-E-YOLO. Given the critical impact of early disease detection on crop yield and quality, the proposed model aims to provide a robust solution for farmers and agricultural experts. The proposed architecture utilizes advanced techniques to improve feature extraction and processing, enabling the model to accurately identify various diseases, including early blight and late blight, as well as healthy leaves. By leveraging deep learning methodologies, the model processes images of potato leave to ensure high accuracy in real-time disease detection. The integration of features such as multi-scale analysis and spatial pooling enhances the model’s ability to adapt to varying sizes and patterns found in disease symptoms, ultimately improving its robustness. The final detection output includes precise localization and classification of diseased regions, accompanied by confidence scores, allowing for timely intervention and management strategies. This enhanced YOLO model serves as an effective tool for monitoring potato crop health, facilitating informed decision-making that can lead to improved yield and sustainability in agricultural practices. PLD-E-YOLO model achieves a superior accuracy 98.8% compared to other models. The results indicate the model’s potential to transform disease management in precision agriculture, offering an automated approach to enhance crop protection and ensure food security.