Machine Learning for Leaf Disease Classification: Data, Techniques and Applications
It addresses the need for improved plant pathology methods in agriculture through a review of existing technologies, but is incremental as it synthesizes prior work without introducing new methods or results.
This paper provides a survey on machine learning techniques, datasets, and applications for leaf disease classification in agriculture, aiming to offer a comprehensive resource for researchers and practitioners in smart agriculture.
The growing demand for sustainable development brings a series of information technologies to help agriculture production. Especially, the emergence of machine learning applications, a branch of artificial intelligence, has shown multiple breakthroughs which can enhance and revolutionize plant pathology approaches. In recent years, machine learning has been adopted for leaf disease classification in both academic research and industrial applications. Therefore, it is enormously beneficial for researchers, engineers, managers, and entrepreneurs to have a comprehensive view about the recent development of machine learning technologies and applications for leaf disease detection. This study will provide a survey in different aspects of the topic including data, techniques, and applications. The paper will start with publicly available datasets. After that, we summarize common machine learning techniques, including traditional (shallow) learning, deep learning, and augmented learning. Finally, we discuss related applications. This paper would provide useful resources for future study and application of machine learning for smart agriculture in general and leaf disease classification in particular.