Application of Visible/Near-Infrared Spectroscopy and Hyperspectral Imaging with Machine Learning for High-Throughput Plant Heavy Metal Stress Phenotyping:A Review
作者机构：School of Information EngineeringHuzhou UniversityHuzhou 313000China College of Mechanical and Electronic EngineeringNanjing Forestry UniversityNanjing 210037China College of Information Science and TechnologyShihezi UniversityShihezi 832003China
出 版 物：《Plant Phenomics》 (植物表型组学（英文）)
年 卷 期：2023年第5卷第4期
页 面：657-672页
核心收录：
学科分类：0710[理学-生物学] 08[工学] 081104[工学-模式识别与智能系统] 0816[工学-测绘科学与技术] 0901[农学-作物学] 0811[工学-控制科学与工程]
基 金：supported by the Research and Development of Cotton Root Phenotype Information Collection Device and Root Detection Technology(grant number:GJHZ202205) the National Natural Science Foundation of China(grant number:62305166) Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources(grant number:2020E10017).
主 题：Infrared consuming precise
摘 要：Heavy metal pollution is becoming a prominent stress on plants.Plants contaminated with heavy metals undergo changes in external morphology and internal structure,and heavy metals can accumulate through the food chain,threatening human health.Detecting heavy metal stress on plants quickly,accurately,and nondestructively helps to achieve precise management of plant growth status and accelerate the breeding of heavy metal-resistant plant varieties.Traditional chemical reagent-based detection methods are laborious,destructive,time-consuming,and costly.The internal and external structures of plants can be altered by heavy metal contamination,which can lead to changes in plants absorption and reflection of light.Visible/near-infrared(V/NIR)spectroscopy can obtain plant spectral information,and hyperspectral imaging(HSI)can obtain spectral and spatial information in simple,speedy,and nondestructive ways.These 2 technologies have been the most widely used high-throughput phenotyping technologies of plants.This review summarizes the application of V/NIR spectroscopy and HSI in plant heavy metal stress phenotype analysis as well as introduces the method of combining spectroscopy with machine learning approaches for high-throughput phenotyping of plant heavy metal stress,including unstressed and stressed identification,stress types identification,stress degrees identification,and heavy metal content estimation.The vegetation indexes,full-range spectra,and feature bands identified by different plant heavy metal stress phenotyping methods are reviewed.The advantages,limitations,challenges,and prospects of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping are discussed.Further studies are needed to promote the research and application of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping.
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