Machine Learning for Leaf Disease Classification: Data, Techniques and
  Applications
Jianping Yao Son N. Tran Samantha Sawyer Saurabh Garg
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.