文献类型： 外文期刊

作者： Zhou, Chunyan ¹ ; Li, Xiaorui ² ; Zhang, Xuejian ² ; Mao, Jiandong ¹ ; Zhao, Hu ¹ ; Zhang, Bai ¹ ;

作者机构： 1.North Minzu Univ, Sch Elect & Informat Engn, Yinchuan, Ningxia, Peoples R China

2.Inst Agr Econ & Informat Technol, Ningxia Acad Agr & Forestry Sci, Yinchuan, Ningxia, Peoples R China

3.Key Lab Atmospher Environm Remote Sensing Ningxia, Yinchuan, Peoples R China

关键词： 1D-CNN; artificial intelligence; hyperspectral; Lycium barbarum mite infestation

期刊名称：INTERNATIONAL JOURNAL OF OPTICS （ 影响因子：1.7； 五年影响因子：1.4 ）

ISSN： 1687-9384

年卷期： 2025 年 2025 卷 1 期

页码：

收录情况： SCI

摘要： Lycium barbarum sprouts, as a green food with significant development potential, are vulnerable to pests and diseases, particularly mite infestation that can cause leaf distortion and deformation, severely impacting yield and quality. Hyperspectral technology, with its exceptional spectral and spatial resolution, has demonstrated great potential in the monitoring of crop pests and diseases. However, its high data dimensionality and feature similarity pose challenges for analysis. In this study, hyperspectral data from Lycium barbarum sprout leaves were collected to establish a dataset representing four levels of pest severity. KNN, RF, SVM, and 1D-CNN classification models were then employed for modeling and predicting pest levels. Experimental results indicated that the 1D-CNN model in deep learning achieved the highest classification accuracy, reaching 96.08%. Traditional machine learning algorithms, such as random forest (RF), also exhibited high accuracy, with 94.12%. In particular, when classifying severe and moderate pest infestations, the 1D-CNN demonstrates superior precision, recall, and F1-score. The study further revealed that hyperspectral technology can effectively detect changes in leaf physiological structure, offering a theoretical foundation for early pest detection. This study presents an efficient, nondestructive technical method for the early detection of pests and diseases in Lycium barbarum sprouts. Additionally, it allows for the determination of varying levels of pest infestation. This will guide farmers in adopting effective control measures, reducing the reliance on chemical pesticides, and enhancing the yield and quality of Lycium barbarum sprouts. The research methodology of this paper provides a scientific foundation for precision agriculture management.