Version 1
: Received: 11 September 2024 / Approved: 11 September 2024 / Online: 12 September 2024 (07:37:15 CEST)
How to cite:
Hu, P.; Wu, J.; Wu, Y.; Zhang, Y. A Review of the Application of Hyperspectral Imaging Technology in Agricultural Crop Economics. Preprints2024, 2024090920. https://doi.org/10.20944/preprints202409.0920.v1
Hu, P.; Wu, J.; Wu, Y.; Zhang, Y. A Review of the Application of Hyperspectral Imaging Technology in Agricultural Crop Economics. Preprints 2024, 2024090920. https://doi.org/10.20944/preprints202409.0920.v1
Hu, P.; Wu, J.; Wu, Y.; Zhang, Y. A Review of the Application of Hyperspectral Imaging Technology in Agricultural Crop Economics. Preprints2024, 2024090920. https://doi.org/10.20944/preprints202409.0920.v1
APA Style
Hu, P., Wu, J., Wu, Y., & Zhang, Y. (2024). A Review of the Application of Hyperspectral Imaging Technology in Agricultural Crop Economics. Preprints. https://doi.org/10.20944/preprints202409.0920.v1
Chicago/Turabian Style
Hu, P., Yanying Wu and Yi Zhang. 2024 "A Review of the Application of Hyperspectral Imaging Technology in Agricultural Crop Economics" Preprints. https://doi.org/10.20944/preprints202409.0920.v1
Abstract
China is a large agricultural country, and the crop economy has 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 rapid non-destructive detection of crop diseases and pests, as well as non-destructive classification and identification of agricultural products.
This paper first provides an overview of the current applications of hyperspectral imaging tech-nology in crops both domestically and internationally. It then summarizes the methods of hyper-spectral data acquisition and application scenarios. Subsequently, it organizes the processing of hyperspectral data for crop disease and pest detection and classification, deriving relevant pre-processing 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 ag-ricultural products. In conclusion, hyperspectral imaging technology can effectively serve the ag-ricultural economy, making agricultural production more intelligent and holding significant im-portance for the development of agriculture in China.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
