Abstract
Plant disease detection is crucial to modern-day agriculture because timely diagnosis can reduce the loss of crops to an appreciable level and improve productivity. This review presents advanced disease detection systems based on machine learning techniques and multimodal data analysis. A comprehensive comparison of different machine learning algorithms, including convolutional neural networks (CNNs), transfer learning models, and object detection methods like YOLO, has been done. This study demonstrates that combining visual data with the analysis of volatile organic compounds (VOC) enhances the accuracy and reliability of the diagnosis. This provides opportunities for the actual development of satellite and cheap systems for monitoring operable in the field. Theoretically, this work contributes to developing strategies for integrating heterogeneous data and optimizing deep neural network models to make them lightweight and effective. The review emphasizes developing scalable and adaptive technologies for plant disease detection within precision agriculture.