Tomato Plant Disease Detection Using Leaf Image Analysis: A Comprehensive Review

Tomato crop diseases have a significant impact on agricultural production, resulting in substantial financial losses and food shortages. Accurate and timely diagnosis can help minimize crop loss; however, traditional manual methods often lack precision and efficiency due to their reliance on human expertise. This review examines the application of advanced image processing and machine learning techniques for automating tomato plant disease detection. It focuses on the effectiveness of Random Forest (RF), K-Nearest Neighbors (KNN), and K-Fold Cross-Validation in improving the classification of diseased leaves.Additionally, the review highlights essential preprocessing techniques, including normalization, noise reduction, and segmentation, which are critical for enhancing image quality. It explores feature extraction methods based on color, texture, and shape for accurate disease identification. Furthermore, the study provides a critical analysis of state-of-the-art algorithms, datasets, and their applications, considering key factors such as accuracy, robustness, and scalability.It also addresses challenges such as dataset variability, geographical factors (latitude and longitude), and real-time processing constraints. Finally, the review discusses potential future research directions, including the development of lightweight models for mobile platforms, the integration of disease detection with treatment recommendations, and their transformative impact on agriculture, sustainability, and global food security.