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.

翻译：可持续发展需求的日益增长推动了一系列信息技术助力农业生产。尤其是作为人工智能分支的机器学习应用，在增强和革新植物病理学方法方面展现了多项突破性进展。近年来，机器学习已被广泛应用于叶片病害分类的学术研究与工业实践。因此，为研究人员、工程师、管理者及企业家提供关于叶片病害检测中机器学习技术与应用最新发展的全面视角具有重大意义。本研究将从数据、技术及应用三个层面对该主题进行综述。本文首先梳理公开数据集，随后总结常见机器学习技术，包括传统（浅层）学习、深度学习与增强学习，最后探讨相关应用案例。本文将为未来机器学习在智慧农业领域（尤其是叶片病害分类）的研究与实践提供有价值的参考资源。