湖北农业科学 ›› 2025, Vol. 64 ›› Issue (10): 213-218.doi: 10.14088/j.cnki.issn0439-8114.2025.10.033

• 信息工程 • 上一篇    下一篇

棉花病虫害智能识别技术研究进展

张友昌1, 张教海1, 牛林涛2, 王孝刚1   

  1. 1. 湖北省农业科学院经济作物研究所/农业农村部长江中游棉花生物学与遗传育种重点实验室,武汉 430070;
    2.湖北银丰棉花股份有限公司,武汉 430012
  • 收稿日期:2025-06-13 出版日期:2025-10-25 发布日期:2025-11-14
  • 通讯作者: 王孝刚(1974-),男,湖北荆州人,研究员,硕士,主要从事棉花栽培生理研究工作,(电话)13237189043(电子信箱)285478220@qq.com。
  • 作者简介:张友昌(1984-),男,湖北武汉人,硕士,主要从事棉花栽培生理研究,(电话)13554075123(电子信箱)337868047@qq.com。
  • 基金资助:
    国家重点研发计划项目(2024YFD2300605); 湖北省农业科技创新中心项目(620-000-001-006)

Research progress on intelligent identification technology for cotton diseases and pests

ZHANG You-chang1, ZHANG Jiao-hai1, NIU Lin-tao2, WANG Xiao-gang1   

  1. 1. Institute of Economic Crops, Hubei Academy of Agricultural Sciences/Key Laboratory of Cotton Biology and Genetic Breeding in the Middle Reaches of the Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan 430070, China;
    2. Hubei Yinfeng Cotton Co.,Ltd., Wuhan 430012, China
  • Received:2025-06-13 Published:2025-10-25 Online:2025-11-14

摘要: 利用智能识别技术及时、准确诊断棉花病虫害。棉花病虫害的智能识别主要采用知识驱动与专家系统技术、可见光图像(传统机器学习与深度学习)技术、多光谱与高光谱技术及多模态数据融合技术。综述了基于上述技术的棉花病虫害智能识别研究进展及当前研究中存在的问题,并展望了棉花病虫害智能识别技术的发展趋势,以推动识别系统向精准化、实时化发展。

关键词: 棉花, 病虫害, 智能识别技术, 研究进展

Abstract: Intelligent identification technology was used to diagnose cotton diseases and pests timely and accurately. The intelligent identification of cotton diseases and pests primarily employed knowledge-driven and expert system technologies, visible light image (traditional machine learning and deep learning) technologies, multispectral and hyperspectral technologies, and multimodal data fusion technologies. Research progress on the intelligent identification of cotton diseases and pests based on the aforementioned technologies was reviewed, along with existing problems in current research, and the development trend of intelligent identification technology for cotton diseases and pests was prospected, to promote the development of identification systems towards precision and real-time performance.

Key words: cotton, diseases and pests, intelligent identification technology, research progress

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