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基于人工智能的作物病害识别研究进展

引用本文：周长建,宋佳,向文胜.基于人工智能的作物病害识别研究进展.植物保护学报,2022,49(1):316-324

DOI：10.13802/j.cnki.zwbhxb.2022.2022803

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作者	单位	E-mail
周长建	东北农业大学, 高性能计算与人工智能研究中心, 哈尔滨 150030 东北农业大学, 黑龙江省农业微生物重点实验室, 哈尔滨 150030
宋佳	东北农业大学, 黑龙江省农业微生物重点实验室, 哈尔滨 150030
向文胜	东北农业大学, 高性能计算与人工智能研究中心, 哈尔滨 150030 东北农业大学, 黑龙江省农业微生物重点实验室, 哈尔滨 150030 中国农业科学院植物保护研究所, 植物病虫害生物学国家重点实验室, 北京 100193	xiangwensheng@neau.edu.cn

中文摘要:传统依靠人工经验的作物病害识别方式难以适应大规模种植环境，迫切需要寻求新的解决方案。近年来，人工智能技术在许多领域取得了丰硕成果，在作物病害识别领域也取得较好的效果。为深入了解人工智能技术在作物病害识别领域中的研究现状，该文主要从传统的机器学习方法和深度学习方法2个角度分析人工智能技术在作物病害识别领域的研究进展，主要包括这2种方法的技术理论、主要工作流程、应用现状及优缺点，同时展望了人工智能技术在未来作物病害识别领域的发展趋势。

中文关键词:植物保护病害识别人工智能机器学习

Research progresses in artificial intelligence-based crop disease identification

Author Name	Affiliation	E-mail
Zhou Zhangjian	High-performance Computing and Artificial Intelligence Research Center, Northeast Agricultural University, Harbin 150030, Heilongjiang Province, China Key Laboratory of Agricultural Microbiology in Heilongjiang Province, Northeast Agricultural University, Harbin 150030, Heilongjiang Province, China
Song Jia	Key Laboratory of Agricultural Microbiology in Heilongjiang Province, Northeast Agricultural University, Harbin 150030, Heilongjiang Province, China
Xiang Wensheng	High-performance Computing and Artificial Intelligence Research Center, Northeast Agricultural University, Harbin 150030, Heilongjiang Province, China Key Laboratory of Agricultural Microbiology in Heilongjiang Province, Northeast Agricultural University, Harbin 150030, Heilongjiang Province, China State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China	xiangwensheng@neau.edu.cn

Abstract:Traditional crop disease identification methods that rely on manual experience are not completely suitable for large-scale growing environments, and it is an urgent to find new solutions. In recent years, with the fruitful achievements of artificial intelligence(AI) technologies in many fields, it has been used in crop disease identification and achieved exciting progresses. In order to gain an in-depth understanding of the progresses of AI in crop disease identification tasks, this paper mainly analyzes the application of AI in crop disease identification from two perspectives:conventional machine learning methods and deep learning methods. The technical theory of these methods, main workflow, application status, advantages and disadvantages of the two methods are also investigated respectively. The trend of crop disease identification in the future is also foreseen at the same time.

keywords:plant protection disease identification artificial intelligence machine learning

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