基于可见-近红外光谱的柑橘叶片功能性氮含量无损监测模型研究  

Non-Destructive Monitoring Model of Functional Nitrogen Content in Citrus Leaves Based on Visible-Near Infrared Spectroscopy

作　　者：杨群 凌琪涵[1] 魏勇 宁强 孔发明 周艺凡 张海琳 王洁 YANG Qun;LING Qi-han;WEI Yong;NING Qiang;KONG Fa-ming;ZHOU Yi-fan;ZHANG Hai-lin;WANG Jie(College of Resources and Environment,Southwest University,Chongqing 400715,China;Interdisciplinary Research Center for Agriculture Green Development in Yangtze River Basin,Southwest University,Chongqing 400715,China)

机构地区：[1]西南大学资源环境学院,重庆400715 [2]西南大学长江经济带农业绿色发展研究中心,重庆400715

出　　处：《光谱学与光谱分析》2023年第11期3396-3403,共8页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(31801932)资助。

摘　　要：柑橘是我国第一大类水果,氮素对于柑橘的生长发育至关重要,实时、无损地监测柑橘氮素营养状况,对于氮素养分精准管理具有重要意义。植株体内的氮素可以分为营养性氮素、结构性氮素和功能性氮素,不同形态氮素各组分在柑橘叶片中的含量对叶片生理生化反应有一定的指示作用,其中,功能性氮含量是指示柑橘氮营养状况的重要指标。以“春见”橘橙为试验材料,分别于果实膨大期和转色期,利用可见-近红外光谱仪测定不同施氮处理的柑橘叶片反射光谱,并用化学分析方法测定其叶片功能性氮含量。分析了柑橘果实膨大期和转色期叶片原始光谱和一阶微分光谱与叶片功能性氮含量的相关关系,筛选出敏感波段,利用全波段和敏感波段,结合光谱植被指数法、光谱化学计量法和机器学习方法,构建了柑橘果实膨大期和转色期叶片功能性氮含量的无损监测模型,并对比分析多种光谱变换和光谱预处理方法对于模型精度的影响。结果表明,在柑橘果实膨大期,对全波段原始光谱进行标准正态化变换预处理,结合反向传播神经网络构建的柑橘叶片功能性氮含量无损监测模型精度较高,其建模集决定系数R^(2)_(c)和验证集决定系数R^(2)_(v)均为0.78,建模集均方根误差RMSEC和验证集均方根误差RMSEV均为0.82 g·kg^(-1);基于敏感波段原始光谱结合随机森林构建的模型精度也较高,其R^(2)_(c)和RMSEC分别为0.84和0.67 g·kg^(-1),R^(2)_(v)和RMSEV分别为0.74和0.83 g·kg^(-1)。在柑橘果实转色期,对全波段原始光谱进行标准正态化变换预处理,结合BPNN构建的柑橘叶片功能性氮含量无损监测模型精度较高,其R^(2)_(c)和RMSEC分别为0.77和1.04 g·kg^(-1),R^(2)_(v)和RMSEV分别为0.76和1.13 g·kg^(-1)。研究表明,可以利用可见-近红外光谱技术,实现对柑橘叶片功能性氮含量的无损监测。Citrus is the largest kind of fruit in China.Nitrogen is very important for the growth and development of citrus.Real-time and non-destructive monitoring of the nitrogen status of citrus is of great significance for accurate management of nitrogen nutrients.Nitrogen in plants can be divided into assimilable nitrogen,structural nitrogen and functional nitrogen.The content of each component of different forms of nitrogen in citrus leaves has a certain indicative effect on the physiological and biochemical reactions of leaves.Among them,the content of functional nitrogen is an important indicator of nitrogen nutrition status in citrus.“Chunjian”orange was used as the experimental material in this study.The reflectance spectra of citrus leaves under different nitrogen treatments were measured by the visible-near infrared spectrometer at the fruit swelling period and fruit coloring period,and the functional nitrogen content in leaves was determined by chemical analysis.The correlation between the original spectrum,first-order differential spectrum and the functional nitrogen content of leaves at the fruit swelling and fruit coloring periods of citrus was analyzed,and the sensitive bands were selected.The non-destructive monitoring model of the functional nitrogen content of leaves at the fruit swelling period and fruit coloring period of citrus was constructed by using the full-band and sensitive bands,combined with the spectral vegetation index method,spectral chemical measurement method and machine learning method,and the effects of various spectral variants and spectral preprocessing methods on the accuracy of the model were compared and analyzed.The results showed that the non-destructive monitoring model of functional nitrogen content in citrus leaves constructed by standard normal variate transformation pretreatment of the full-band original spectrum combined with the backpropagation neural network had high accuracy during thefruit swelling period.The calibration set determination coefficient R^(2)_(c) and v

关 键 词：柑橘 功能性氮 可见-近红外光谱 反向传播神经网络 随机森林 

分 类 号：O657.3[理学—分析化学]