Cross-comparative review of Machine learning for plant disease detection: apple, cassava, cotton and potato plants

Plant disease detection has played a significant role in combating plant diseases that pose a threat to global agriculture and food security. Detecting these diseases early can help mitigate their impact and ensure healthy crop yields. Machine learning algorithms have emerged as powerful tools for accurately identifying and classifying a wide range of plant diseases from trained image datasets of affected crops. These algorithms, including deep learning algorithms, have shown remarkable success in recognizing disease patterns and early signs of plant diseases. Besides early detection, there are other potential benefits of machine learning algorithms in overall plant disease management, such as soil and climatic condition predictions for plants, pest identification, proximity detection, and many more. Over the years, research has focused on using machine-learning algorithms for plant disease detection. Nevertheless, little is known about the extent to which the research community has explored machine learning algorithms to cover other significant areas of plant disease management. In view of this, we present a cross-comparative review of machine learning algorithms and applications designed for plant disease detection with a specific focus on four (4) economically important plants: apple, cassava, cotton, and potato. We conducted a systematic review of articles published between 2013 and 2023 to explore trends in the research community over the years. After filtering a number of articles based on our inclusion criteria, including articles that present individual prediction accuracy for classes of disease associated with the selected plants, 113 articles were considered relevant. From these articles, we analyzed the state-of-the-art techniques, challenges, and future prospects of using machine learning for disease identification of the selected plants. Results from our review show that deep learning and other algorithms performed significantly well in detecting plant diseases. In addition, we found a few references to plant disease management covering prevention, diagnosis, control, and monitoring. In view of this, little or no work has explored the prediction of the recovery of affected plants. Hence, we propose opportunities for developing machine learning-based technologies to cover prevention, diagnosis, control, monitoring, and recovery.