This paper explores the application of artificial intelligence (AI) in revolutionizing plant disease detection, focusing on four major crops: tomato, chili, potato, and cucumber. The authors highlight the limitations of traditional methods, which are time-consuming, resource-intensive, and require expertise, and emphasize the need for automated solutions. They discuss the use of AI, particularly machine learning (ML) and deep learning (DL), to enhance the accuracy and efficiency of disease detection. The paper outlines the steps involved in automated disease detection, including image acquisition, preprocessing, segmentation, feature extraction, and classification. It reviews various ML and DL models, such as CNNs, and their performance in detecting and classifying diseases in these crops. The authors also address the challenges and limitations of AI in disease detection, such as noise in images, environmental factors affecting image acquisition, and the difficulty in identifying and isolating disease symptoms. Finally, they propose future research directions and potential solutions to overcome these challenges, emphasizing the importance of integrating AI with IoT platforms for more effective field-based disease detection and monitoring.This paper explores the application of artificial intelligence (AI) in revolutionizing plant disease detection, focusing on four major crops: tomato, chili, potato, and cucumber. The authors highlight the limitations of traditional methods, which are time-consuming, resource-intensive, and require expertise, and emphasize the need for automated solutions. They discuss the use of AI, particularly machine learning (ML) and deep learning (DL), to enhance the accuracy and efficiency of disease detection. The paper outlines the steps involved in automated disease detection, including image acquisition, preprocessing, segmentation, feature extraction, and classification. It reviews various ML and DL models, such as CNNs, and their performance in detecting and classifying diseases in these crops. The authors also address the challenges and limitations of AI in disease detection, such as noise in images, environmental factors affecting image acquisition, and the difficulty in identifying and isolating disease symptoms. Finally, they propose future research directions and potential solutions to overcome these challenges, emphasizing the importance of integrating AI with IoT platforms for more effective field-based disease detection and monitoring.