Zhao Junmin, Xu Laixiang, Ma Zizhen, Li Juncai, Wang Xiaowei, Liu Yunchang, Du Xiaojie
School of Computer and Data Science, Research Center of Smart City and Big Data Engineering of Henan Province, Henan University of Urban Construction, Pingdingshan, China.
School of Computer and Data Science, Research Center of Smart City and Big Data Engineering of Henan Province, Innovation Laboratory of Smart Transportation and Big Data Development of Henan Province, Henan University of Urban Construction, Pingdingshan, China.
Front Plant Sci. 2025 Aug 20;16:1637241. doi: 10.3389/fpls.2025.1637241. eCollection 2025.
Plant leaf disease control is crucial given the prevalence of plant leaf diseases around the world. The most crucial aspect of controlling plant leaf diseases is appropriately identifying them. Deep learning-based plant leaf disease recognition is a viable alternative to artificial methods that are useless and inaccurate. The proposed work aims to combine plant leaf disease datasets from various countries, review current research and progress in deep learning algorithms for plant disease recognition, and explain how different types of data are developed and used in this area using different deep learning networks. The feasibility of several network models for deep learning-based plant leaf disease detection is discussed. Solving shortcomings such as sunlight irradiation in plant planting conditions, similar disease incidence of different plant leaf diseases, and varied symptoms of the same disease in different damage periods or infection degrees are all essential study topics in the growth of this discipline. To address the concerns raised above and establish the field's future development potential, we must research high-performance neural networks based on the benefits and downsides of diverse networks. The proposed work can serve as a foundation for future research and breakthroughs in the identification of plant leaf diseases.
鉴于植物叶片病害在全球范围内的普遍存在，植物叶片病害防治至关重要。控制植物叶片病害最关键的方面是正确识别它们。基于深度学习的植物叶片病害识别是无用且不准确的人工方法的可行替代方案。拟开展的工作旨在整合来自不同国家的植物叶片病害数据集，回顾深度学习算法在植物病害识别方面的当前研究和进展，并解释如何使用不同的深度学习网络在该领域开发和使用不同类型的数据。讨论了几种基于深度学习的植物叶片病害检测网络模型的可行性。解决植物种植条件下的阳光照射、不同植物叶片病害的相似发病率以及同一病害在不同损伤时期或感染程度下的不同症状等缺点，都是该学科发展中的重要研究课题。为了解决上述问题并确立该领域未来的发展潜力，我们必须基于不同网络的优缺点研究高性能神经网络。拟开展的工作可为未来植物叶片病害识别研究和突破奠定基础。
