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Advances and Developments in Monitoring and Inversion of the Biochemical Information of Crop Nutrients Based on Hyperspectral Technology

Authors :
Yali Zhang
Junqi Xiao
Kangting Yan
Xiaoyang Lu
Wanjian Li
Haoxin Tian
Linlin Wang
Jizhong Deng
Yubin Lan
Source :
Agronomy, Vol 13, Iss 8, p 2163 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Crop nutrient biochemical information (mainly including chlorophyll class and nutrient elements mainly nitrogen, phosphorus and potassium) is an important basis for revealing crop growth and development patterns and their relationship with the environment. Hyperspectral technology has been rapidly developed and applied in crop nutrient biochemical information monitoring research. This paper firstly describes the theoretical basis of hyperspectral technology for monitoring crop nutrients and biochemical information. Then, the research progress of hyperspectral technology in monitoring nutrient and biochemical information of crops in different growth periods or different growth environments is outlined. Meanwhile, the shortcomings of the current technology in these research directions and the future research trends are discussed. Finally, the modeling methods for building crop nutrient biochemical information monitoring models by applying hyperspectral data are systematically outlined. And the effects of different spectral pre-processing methods, spectral effective information extraction methods and modeling algorithms on the accuracy of monitoring models are analyzed. On this basis, the challenges and prospects of hyperspectral technology in monitoring crop nutrient biochemical information are presented, aiming to provide relevant theoretical basis and technical reference for the research related to monitoring and inversion of crop physiological parameters based on hyperspectral technology.

Details

Language :
English
ISSN :
20734395
Volume :
13
Issue :
8
Database :
Directory of Open Access Journals
Journal :
Agronomy
Publication Type :
Academic Journal
Accession number :
edsdoj.58d26986057f4b3cb27400f0eb544a5c
Document Type :
article
Full Text :
https://doi.org/10.3390/agronomy13082163