Redefining Agricultural Disease Management using AI and ML

Precision agriculture is a fast growing field that may contemporary issues surrounding agricultural sustainability are increasingly being addressed through innovative technologies. Machine learning, a cutting-edge tool that enhances precision agriculture, enables sophisticated methods for detecting and classifying plant diseases. This paper offers a comprehensive review of how ML and DL techniques are applied in precision agriculture, focusing on disease identification and categorization. A novel classification framework is introduced, organizing relevant studies into distinct categories—Method-wise Classification Based Approaches. First, we categorize the studies method wise into classification (e.,g, image, and video-based studies) or object detection (e. g., bounding box-based studies) algorithms. Moreover, we provide existing archives for the classification and tracking of plant infections. By presenting a systematic framework that groups machine learning techniques according to their methodology, this study seeks to close the gap between the current plant disease classification techniques. We offer a comparative study to identify the best methods for disease identification by assessing the effectiveness of cutting-edge classification and object recognition algorithms on the PlantDoc dataset. In addition to streamlining precision agriculture research, the suggested architecture provides a basis for creating plant disease detection models that are more precise and scalable.