文献类型：期刊文献

英文题名：A Review of the Application of Hyperspectral Imaging Technology in Agricultural Crop Economics

作者：Wu, Jinxing Zhang, Yi Hu, Pengfei Wu, Yanying

第一作者：Wu, Jinxing

通信作者：Hu, PF[1]

机构：[1]Guizhou Minzu Univ, Sch Phys & Mechatron Engn, Guiyang 550025, Peoples R China;[2]Off Acad Res, Sch Equipment Mfg Polytech, Guiyang 550008, Peoples R China;[3]Guizhou Inst Technol, Sch Mech Engn, Guiyang 550003, Peoples R China

第一机构：Guizhou Minzu Univ, Sch Phys & Mechatron Engn, Guiyang 550025, Peoples R China

通信机构：corresponding author), Off Acad Res, Sch Equipment Mfg Polytech, Guiyang 550008, Peoples R China.

年份：2024

卷号：14

期号：10

外文期刊名：COATINGS

收录：;Scopus(收录号：2-s2.0-85207681654);WOS:【SCI-EXPANDED(收录号:WOS:001342479900001)】；

基金：Natural Science Foundation of Guizhou Minzu University (No. GZMUZK [2022] YB01); Doctoral funding project of Guizhou Minzu University GZMUZK [2024] QD72; Guizhou Province Science and Technology Support Program Project: [2022] 165; Guiyang Science and Technology Support Plan Project: [2022] 3-8; Guizhou Provincial Department of Education supports the scientific research platform [2022] 014.

语种：英文

外文关键词：hyperspectral; crop economy; non-destructive detection; classification

摘要：China is a large agricultural country, and the crop economy holds an important place in the national economy. The identification of crop diseases and pests, as well as the non-destructive classification of crops, has always been a challenge in agricultural development, hindering the rapid growth of the agricultural economy. Hyperspectral imaging technology combines imaging and spectral techniques, using hyperspectral cameras to acquire raw image data of crops. After correcting and preprocessing the raw image data to obtain the required spectral features, it becomes possible to achieve the rapid non-destructive detection of crop diseases and pests, as well as the non-destructive classification and identification of agricultural products. This paper first provides an overview of the current applications of hyperspectral imaging technology in crops both domestically and internationally. It then summarizes the methods of hyperspectral data acquisition and application scenarios. Subsequently, it organizes the processing of hyperspectral data for crop disease and pest detection and classification, deriving relevant preprocessing and analysis methods for hyperspectral data. Finally, it conducts a detailed analysis of classic cases using hyperspectral imaging technology for detecting crop diseases and pests and non-destructive classification, while also analyzing and summarizing the future development trends of hyperspectral imaging technology in agricultural production. The non-destructive rapid detection and classification technology of hyperspectral imaging can effectively select qualified crops and classify crops of different qualities, ensuring the quality of agricultural products. In conclusion, hyperspectral imaging technology can effectively serve the agricultural economy, making agricultural production more intelligent and holding significant importance for the development of agriculture in China.