Abstract— Various techniques have revolutionized the field of plant disease detection, offering accurate approaches for timely detection and recognition of crop diseases. This comprehensive review explores the current utilization of diverse techniques for plant disease detection and classification. It analyzes recent publications, considering aspects such as disease detection methods and dataset characteristics. These techniques have significantly advanced object detection and recognition in agriculture, facilitating efficient crop management and higher yields. However, the complexity of identifying and detecting plant diseases from images necessitates species-specific detection for customized control strategies. This study discusses the challenges and proposed solutions associated with the use of different techniques in early disease detection concentrated on deep learning methods. Overall, the review demonstrates the considerable potential of these techniques in disease detection and emphasizes the ongoing need for research and development to address current challenges and optimize their benefits in agriculture. and also underscores the importance of incorporating emerging technologies and data-driven approaches to further enhance the precision and scalability of plant disease detection systems.