多光谱成像技术在植物学研究中的应用

doi:10.11983/CBB21002

摘要/Abstract

摘要： 多光谱成像(MSI)技术是一种新兴的成像检测技术, 通过将光谱与成像合二为一, 可实现植物结构、生理、生化表型的定性定量分析及其特征分布的评估。近年来, 与数学建模分析结合的MSI技术具有强大的信息捕获能力, 在植物学研究中展现出强劲的应用潜力。该文介绍了MSI技术的成像原理, 总结了近年来MSI技术在植物损伤鉴定、病害研究、代谢物质生化特征及生理进程鉴定方面的应用, 展望了该技术在植物研究领域的前沿性发展, 以期使MSI技术在植物研究中得到更好的应用。

关键词: 多光谱成像, 光谱, 数学建模, 精度评价, 植物研究

Abstract: Multispectral imaging (MSI) is an emerging technology designed for advanced imaging detection, which combines the information of spectroscopy and imaging to conduct qualitative and quantitative analysis of plant phenotypes including structural, physiological and biochemical characteristics. Recently, MSI shows a strong capability to capture detailed spectral information in combination with the applications of mathematical modeling and analysis, and displays a strong potential in the field of plant research. Here we introduce the principle of MSI technology and summarize the main applications of this technology in various aspects of plant research, which includes detection of plant damage and disease, identification of plant metabolites and characterizing plant physiological status. We alse prospect the frontier development of MSI in plant research.

Key words: multispectral imaging, spectroscopy, modeling, accuracy, plant science

王众司, 贾亚萍, 张瑾, 王若涵. 多光谱成像技术在植物学研究中的应用. 植物学报, 2021, 56(4): 500-508.

Zhongsi Wang, Yaping Jia, Jin Zhang, Ruohan Wang. Multispectral Imaging and Its Applications in Plant Science Research. Chinese Bulletin of Botany, 2021, 56(4): 500-508.

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链接本文: https://www.chinbullbotany.com/CN/10.11983/CBB21002

https://www.chinbullbotany.com/CN/Y2021/V56/I4/500

图/表 3

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应用	波长范围(nm)	关键波长(nm)	应用模型	精度	成像优势	参考文献
植物表型鉴定	446, 452, 473, 505, 524, 534, 568, 594, 673, 704, 715, 734, 949	-	PLSR (偏最小二乘回归); PLS-DA (偏最小二乘判别分析); SPA (连续投影算法); RF (随机森林)	0.79	玉米表型高通量鉴定的新方法	Feng et al., 2018
植物病害检测	365-960 350-2500	520, 540, 580, 610, 630, 650, 770	SVM (支持向量机); OLS (普通最小二乘法)	平均高于0.85	可检测未产生肉眼可见变化时的病害	Veys et al., 2019; Liu et al., 2020a
果实品质鉴定	325-1100 550-950	640, 670, 760	LDA (线性判别式分析); K- NN (K-近邻); FCMA (模糊C-均值聚类算法)	平均高于0.97	柑橘类水果高通量品质鉴定的新方法	Wei et al., 2017; Li et al., 2017
微生物侵染检测	400-1000 375-1600 475, 560, 668, 840, 717 382-1032 400-1100	396, 578, 741, 420, 631, 990; 494, 578, 639, 678; 840; 475.56, 548.91, 652.14, 516.31, 720.05, 915.64	ANOVA (方差分析); Kruskal-Wallis检验; LDA; QDA (二次判别分析); PLS (偏最小二乘法); LR (线性回归); SVM; PCA (主成分分析)	平均高于0.93	可检测未产生肉眼可见变化时的侵染	Vrešak et al., 2016; Yin et al., 2017; Nagasubramanian et al., 2018; Baek et al., 2019a; Fahrentrapp et al., 2019
植物损伤鉴定	375-970 650-830	-	SVM; RF; CNN (卷积神经网络)	平均高于0.89	可检测未产生肉眼可见变化时的损伤	Lu and Lu, 2018; Salimi and Boelt, 2019
生理状态检测	405, 560, 660, 860 400-1100 375, 405, 435, 450, 470, 505, 525, 570, 590, 630, 645, 660, 700, 780, 850, 870, 890, 910, 940, 970 400-1000 380-2200	510, 550 700, 800, 900, 1000	LR (线性回归); RC (相关曲线); PLS (偏最小二乘法); MLR (多元线性回归); LDA; PCA (主成分分析); OTSU (最大类间方差); BPANN (反向传播人工神经网络); SVMR (支持向量机回归)	平均高于0.96	可实现植物无机元素、代谢物的无损检测	Lara et al., 2016; Khodabakhshian et al., 2017; Wang et al., 2018; ElMasry et al., 2019a; Wang et al., 2020
种子鉴定	405, 470, 530, 590, 660, 850 1000-2500	-	MLR (多元线性回归); PLS-DA	平均高于0.97	可实现种子生活力或化学处理的非侵入式检测	Mcdonald et al., 2019; Baek et al., 2019b

应用	波长范围(nm)	关键波长(nm)	应用模型	精度	成像优势	参考文献
植物表型鉴定	446, 452, 473, 505, 524, 534, 568, 594, 673, 704, 715, 734, 949	-	PLSR (偏最小二乘回归); PLS-DA (偏最小二乘判别分析); SPA (连续投影算法); RF (随机森林)	0.79	玉米表型高通量鉴定的新方法	Feng et al., 2018
植物病害检测	365-960 350-2500	520, 540, 580, 610, 630, 650, 770	SVM (支持向量机); OLS (普通最小二乘法)	平均高于0.85	可检测未产生肉眼可见变化时的病害	Veys et al., 2019; Liu et al., 2020a
果实品质鉴定	325-1100 550-950	640, 670, 760	LDA (线性判别式分析); K- NN (K-近邻); FCMA (模糊C-均值聚类算法)	平均高于0.97	柑橘类水果高通量品质鉴定的新方法	Wei et al., 2017; Li et al., 2017
微生物侵染检测	400-1000 375-1600 475, 560, 668, 840, 717 382-1032 400-1100	396, 578, 741, 420, 631, 990; 494, 578, 639, 678; 840; 475.56, 548.91, 652.14, 516.31, 720.05, 915.64	ANOVA (方差分析); Kruskal-Wallis检验; LDA; QDA (二次判别分析); PLS (偏最小二乘法); LR (线性回归); SVM; PCA (主成分分析)	平均高于0.93	可检测未产生肉眼可见变化时的侵染	Vrešak et al., 2016; Yin et al., 2017; Nagasubramanian et al., 2018; Baek et al., 2019a; Fahrentrapp et al., 2019
植物损伤鉴定	375-970 650-830	-	SVM; RF; CNN (卷积神经网络)	平均高于0.89	可检测未产生肉眼可见变化时的损伤	Lu and Lu, 2018; Salimi and Boelt, 2019
生理状态检测	405, 560, 660, 860 400-1100 375, 405, 435, 450, 470, 505, 525, 570, 590, 630, 645, 660, 700, 780, 850, 870, 890, 910, 940, 970 400-1000 380-2200	510, 550 700, 800, 900, 1000	LR (线性回归); RC (相关曲线); PLS (偏最小二乘法); MLR (多元线性回归); LDA; PCA (主成分分析); OTSU (最大类间方差); BPANN (反向传播人工神经网络); SVMR (支持向量机回归)	平均高于0.96	可实现植物无机元素、代谢物的无损检测	Lara et al., 2016; Khodabakhshian et al., 2017; Wang et al., 2018; ElMasry et al., 2019a; Wang et al., 2020
种子鉴定	405, 470, 530, 590, 660, 850 1000-2500	-	MLR (多元线性回归); PLS-DA	平均高于0.97	可实现种子生活力或化学处理的非侵入式检测	Mcdonald et al., 2019; Baek et al., 2019b