基于不同传感器的纽荷尔脐橙叶片叶绿素含量检测技术评价

doi:10.3864/j.issn.0578-1752.2018.06.005

摘要/Abstract

摘要： 【目的】研究基于不同传感器的叶片叶绿素含量监测方法，探索建立轻简、高效的柑橘叶绿素含量监测技术。【方法】以枳砧纽荷尔脐橙当年生春梢叶片为试材，采用便携式地物光谱仪FieldSpec4、数字图像技术、荧光及多酚含量测量仪Multiplex?Research和SPAD 502分别获取叶片光谱反射率、图像信息、荧光值和叶绿素含量，分析各数字化指标、叶片光谱指标、荧光值与叶绿素含量（SPAD值）的相关性。基于不同指标采用偏最小二乘法（partial least squares regression，PLS）及内部交叉验证构建叶绿素含量定量反演模型，并进行模型精度检验以及各传感器监测叶片叶绿素含量的可行性评价。【结果】基于地物光谱仪FieldSpec4、荧光及多酚含量测量仪Multiplex?Research、数字图像技术获得参数信息与叶片叶绿素含量均呈极显著相关，模型拟合度较好，相关系数均在0.7以上。其中，通过地物光谱仪获取的532 nm、586 nm与705 nm特征波段光谱数据经一阶导数（FD）预处理建立的偏最小二乘法（PLS）预测模型效果最优，MRE=1.80%，RPD=3.801。其次为基于数字图像分析技术以G-B作为特征参数建立一元二次回归模型，MRE=1.98%，RPD=3.946。以及基于荧光及多酚含量测量仪Multiplex?Research获取的特征荧光参数建立PLS模型，MRE=2.37%，RPD=4.807。【结论】地物光谱仪、Multiplex?Research、数字图像技术均可应用于纽荷尔脐橙叶片叶绿素含量的估测。其中，以地物光谱仪FieldSpec4预测精度相对最优，Multiplex?Research便捷程度最高，数字图像技术用于大批量样品的测定操作性相对较强。

关键词: 纽荷尔脐橙, 叶绿素含量, 反射光谱, 荧光光谱, 数字图像

Abstract: 【Objective】 The objective of the study was to investigate the detection accuracy of chlorophyll content in the leaves of Newhall Navel Orange (Citrus sinensis Osbeck) based on different sensors with different scales and prediction models, so as to provide evidence for establishing an easy and high efficient nutrition diagnosis technology in citrus orchard.【Method】The spring shoot leaves of Newhall Navel Orange trees on trifoliate orange (Poncirus trifoliata (L.) Raf.) root stocks were selected for the study. Using the FieldSpec4, Multiplex®Research, digital image technology and SPAD-502 chlorophyll meter to obtain the leaf spectral, fluorescent values, digital indicators and chlorophyll content (SPAD values). In addition, the association of leaf spectral, fluorescent values and digital indicators with chlorophyll content were analyzed. The chlorophyll content quantitative inversion model was established by means of univariate and multiple regression (partial least square regression, PLS). Then the model accuracy and feasibility of the monitoring of leaf chlorophyll content based on different sensors were evaluated. 【Result】The results showed that the correlation between the content of relative chlorophyll content and parameter information from FieldSpec4, Multiplex®Research, digital image technology were extremely significant, and their relative coefficients were all more than 0.7. The goodness-of-fit of the predictive models were good. Among them, the PLS models based on FieldSpec4 established by First-order derivative (FD) method at the band of 532 nm, 586 nm and 705 nm showed the best goodness-of-fit with MRE (mean relative error) =1.80%，RPD (residual prediction deviation) =3.801. The quadratic regression model based on digital image technology which chose G-B values as characteristic indicators was established with MRE=1.98%, RPD=3.946. The PLS models was established by using the characteristic fluorescent parameters based on Multiplex®Research with MRE=2.37%, RPD=4.807.【Conclusion】The results showed that it had a certain feasibility by using information obtained from FieldSpec4, Multiplex® Research, digital image technology to predict chlorophyll content of citrus single leaves. The prediction accuracy of FieldSpec4 was best, the Multiplex ® Research was most convenient, and the digital image technology was more suitable for detection of large quantities of samples.

Key words: Newhall Navel Orange, chlorophyll content, spectrum, fluorescence, digital image

李文涛，杨江波，张绩，王克健，邓烈，吕强，何绍兰，谢让金，郑永强，马岩岩，易时来. 基于不同传感器的纽荷尔脐橙叶片叶绿素含量检测技术评价[J]. 中国农业科学, 2018, 51(6): 1057-1066.

LI WenTao, YANG JiangBo, ZHANG Ji, WANG KeJian, DENG Lie, Lü Qiang, HE ShaoLan, XIE RangJin, ZHENG YongQiang, MA YanYan, YI ShiLai. The Evaluation of Chlorophyll Content Detection in the Leaves of Newhall Navel Orange Based on Different Sensors[J]. Scientia Agricultura Sinica, 2018, 51(6): 1057-1066.

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