This study explores the application of artificial intelligence (AI) in plant disease detection, focusing on four major crops: tomato, chili, potato, and cucumber. The research highlights the most common diseases affecting these crops, their symptoms, and the challenges in detecting and classifying them. It provides a detailed overview of the predetermined steps for automated disease detection using AI, including image acquisition, preprocessing, segmentation, feature selection, and classification. The study discusses various machine learning (ML) and deep learning (DL) models used for disease detection, along with the datasets used to evaluate these models. It also identifies the challenges associated with applying AI in disease detection and outlines future research directions, including the integration of AI with IoT platforms such as smart drones for field-based disease detection and monitoring. The study emphasizes the importance of accurate and rapid plant disease detection in enhancing agricultural yield and preventing economic losses. Traditional methods for plant disease detection are time-consuming, require expertise, and are resource-intensive. Therefore, automated disease diagnosis using AI and IoT sensors is considered a promising solution. The research presents a comprehensive examination of existing ML and DL-based studies for detecting diseases in the four crops, including the datasets used to evaluate these studies. It also provides a list of plant disease detection datasets and discusses various ML and DL application problems, along with future research prospects. The study outlines the challenges in applying AI for plant disease detection, including noise and background analysis, factors influencing image acquisition, and the need for robust feature extraction and classification methods. It also discusses the limitations of current AI models in detecting plant diseases and suggests future research directions to improve the accuracy and efficiency of disease detection systems. The study concludes that the integration of AI with IoT platforms can significantly enhance the effectiveness of disease detection and monitoring in agriculture.This study explores the application of artificial intelligence (AI) in plant disease detection, focusing on four major crops: tomato, chili, potato, and cucumber. The research highlights the most common diseases affecting these crops, their symptoms, and the challenges in detecting and classifying them. It provides a detailed overview of the predetermined steps for automated disease detection using AI, including image acquisition, preprocessing, segmentation, feature selection, and classification. The study discusses various machine learning (ML) and deep learning (DL) models used for disease detection, along with the datasets used to evaluate these models. It also identifies the challenges associated with applying AI in disease detection and outlines future research directions, including the integration of AI with IoT platforms such as smart drones for field-based disease detection and monitoring. The study emphasizes the importance of accurate and rapid plant disease detection in enhancing agricultural yield and preventing economic losses. Traditional methods for plant disease detection are time-consuming, require expertise, and are resource-intensive. Therefore, automated disease diagnosis using AI and IoT sensors is considered a promising solution. The research presents a comprehensive examination of existing ML and DL-based studies for detecting diseases in the four crops, including the datasets used to evaluate these studies. It also provides a list of plant disease detection datasets and discusses various ML and DL application problems, along with future research prospects. The study outlines the challenges in applying AI for plant disease detection, including noise and background analysis, factors influencing image acquisition, and the need for robust feature extraction and classification methods. It also discusses the limitations of current AI models in detecting plant diseases and suggests future research directions to improve the accuracy and efficiency of disease detection systems. The study concludes that the integration of AI with IoT platforms can significantly enhance the effectiveness of disease detection and monitoring in agriculture.