植物保护学报2026，Vol.53Issue(1)：95-110,16.DOI:10.13802/j.cnki.zwbhxb.2026.2026807
多光谱技术在果树病害检测中的应用与展望
Application and prospects of multispectral technology in fruit tree disease detection
摘要
Abstract
Multispectral technology,as a core technology at the intersection of computer vision and agri-cultural remote sensing,is driving innovation in orchard pest and disease detection methods and the implementation of precision management.In recent years,the integration of this technology with deep learning models has led to significant progress in the accurate identification of tree diseases and pests,demonstrating distinct advantages in cost-effectiveness,applicability,and real-time monitoring.This paper systematically reviews the applications of multispectral technology in the detection of various tree diseases.For example,multispectral remote sensing in the 520-920 nm wavelength range has been used to detect fire blight in pear trees,achieving a detection accuracy of 95.0%;unmanned aerial vehicle(UAV)-based multispectral imagery in the 475,560,668,717,and 840 nm bands has achieved a maxi-mum detection accuracy of 95.2%for ink disease in chestnut trees;furthermore,multispectral imaging technology integrating multi-color fluorescence and reflectance bands has achieved a detection accuracy of 92.1%for citrus huanglongbing.When combined with models such as Support Vector Machines(SVM),Random Forest,and the improved Mask R-CNN V3,multispectral technology has further enhanced detection accuracy and efficiency across multiple disease types.The SVM model achieved a detection accuracy of 96.60%in wild blueberry disease classification;the Random Forest model achieved an accuracy of 86.46%in detecting yellow leaf disease in betel nut trees;and the improved Mask R-CNN V3 model achieved a detection accuracy of 93.37%for citrus huanglongbing.In addition,by capturing spectral information across multiple bands,multispectral technology can effectively reflect physiological states such as leaf pigment content,providing a scientific basis for early disease diagno-sis.In the future,this technology may further enhance model generalization through integration with machine learning algorithms and,in combination with miniaturized sensors and embedded computing platforms,enable the development of lightweight real-time detection devices for early warning and pre-cise control of orchard diseases,thereby providing important technical support for smart agriculture.关键词
多光谱技术/果园病害检测/数据处理/深度学习Key words
multispectral technology/orchard disease detection/data processing/deep learning引用本文复制引用
岳柳羊,何雄奎,苏立阳,陈恒,王惟实,刘亚佳..多光谱技术在果树病害检测中的应用与展望[J].植物保护学报,2026,53(1):95-110,16.基金项目
国家自然科学基金项目(31761133019),国家现代农业产业技术体系资助项目(CARS-28),中国农业大学2115人才培育发展支持计划项目(2115-89052) （31761133019）
