[1]王宇,汪泓,肖玖军,等.基于MCC-GAPLS-PLSR的辣椒叶绿素含量高光谱定量反演[J].江苏农业学报,2024,(05):865-873.[doi:doi:10.3969/j.issn.1000-4440.2024.05.011]
　WANG Yu,WANG Hong,XIAO Jiujun,et al.Hyperspectral quantitative inversion of chlorophyll content in pepper based on MCC-GAPLS-PLSR[J].,2024,(05):865-873.[doi:doi:10.3969/j.issn.1000-4440.2024.05.011]
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基于MCC-GAPLS-PLSR的辣椒叶绿素含量高光谱定量反演()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2024年05期
页码:
865-873
栏目:
农业信息工程
出版日期:
2024-05-30

文章信息/Info

Title:
Hyperspectral quantitative inversion of chlorophyll content in pepper based on MCC-GAPLS-PLSR
作者:
王宇1; 汪泓2; 肖玖军3; 4; 5; 邢丹6; 李可相3; 4; 张永亮2; 岳延滨7
(1.中国电建集团贵阳勘测设计研究院有限公司,贵州贵阳550081；2.贵州大学矿业学院,贵州贵阳550025；3.贵州科学院山地资源研究所,贵州贵阳550001；4.贵州省土地绿色整治工程研究中心,贵州贵阳550001;5.贵州大学资源与环境工程学院，贵州贵阳550025；6.贵州省农业科学院辣椒研究所,贵州贵阳550009；7.贵州省农业科学院农业科技信息研究所,贵州贵阳550009)
Author(s):
WANG Yu1; WANG Hong2; XIAO Jiujun3; 4; 5; XING Dan6; LI Kexiang3; 4; ZHANG Yongliang2; YUE Yanbin7
(1.Guiyang Engineering Corporation Limited, Power China, Guiyang 550081, China；2.College of Mining, Guizhou University, Guiyang 550025, China；3.Institute of Mountain Resources, Guizhou Academy of Sciences, Guiyang 550001, China；4.Engineering Research Center for Land Green Consolidation of Guizhou, Guiyang 550001, China;5.College of Resources and Environmental Engineering, Guizhou University, Guiyang 550025, China；6.Pepper Research Institute, Guizhou Academy of Agricultural Sciences, Guiyang 550009, China；7.Institute of Agricultural Science and Technology Information, Guizhou Academy of Agricultural Sciences, Guiyang 550009, China)
关键词:
叶绿素含量; 辣椒; 高光谱; 光谱变换; 遗传算法-偏最小二乘法
Keywords:
chlorophyll content; pepper; hyperspectral; spectral transformation; genetic algorithm-partial least squares
分类号:
S127；S641.3
DOI:
doi:10.3969/j.issn.1000-4440.2024.05.011
摘要:
为了准确监测辣椒生长,本研究对辣椒冠层光谱反射率进行对数处理、倒数处理、倒数的对数处理、连续统去除处理、一阶微分处理、二阶微分处理,并与SPAD值进行相关性分析,用最大相关系数法(MCC)选取相关性较好的特征波段生成特征波段数据集,再用遗传算法-偏最小二乘法(GAPLS)进行降维得到最优特征波段组合,采用偏最小二乘法(PLSR)、反向传播神经网络(BPNN)、随机森林(RF)和最小二乘支持向量机(LSSVM)4种机器学习算法构建辣椒叶绿素含量反演模型。结果表明,最优波段和对应处理分别为700 nm（原始光谱）、699 nm（对数处理）、713 nm（连续统去除处理）、500 nm（二阶微分处理）、713 nm（二阶微分处理）。GAPLS的降维效果较好,与降维前相比PLSR模型的精度提升率最高,R2、RPD分别提升了82.22%、136.98%,RMSE降低了29.96%。4种模型中,GAPLS降维处理后的PLSR模型的精度最好,R2、RMSE和RPD分别为0.82、1.94、4.55。本研究构建的MCC-GAPLS-PLSR模型具有较好的反演潜力,适用于研究区辣椒叶片叶绿素含量测定,推动辣椒高效种植。
Abstract:
In order to accurately monitor the growth of peppers, this study performed logarithmic treatment, reciprocal treatment, reciprocal logarithmic treatment, continuum removal treatment, first derivative treatment, second derivate treatment on the canopy spectral reflectance of peppers, and conducted correlation analysis with SPAD values. The maximum correlation coefficient method (MCC) was used to select the feature bands with good correlation to generate a feature band dataset. And the genetic algorithm-partial least squares (GAPLS) was used to reduce the dimensionality to obtain the optimal feature band combination. Pepper chlorophyll content inversion model was constructed by using four machine learning algorithms: partial least squares regression (PLSR), backpropagation neural network (BPNN), random forest (RF) and least squares support vector machine (LSSVM). The results showed that the optimal wavelengths and corresponding treatments were 700 nm (original reflectivity), 699 nm (logarithmic treatment), 713 nm (continuum removal treatment), 500 nm (second derivate treatment), 713 nm (second derivate treatment). The dimensionality reduction effect by GAPLS was good. And compared with before dimension reduction, the accuracy improvement rate of PLSR model was the highest, R2 and RPD increased by 82.22% and 136.98% respectively, and RMSE decreased by 29.96%. Among the four models, PLSR model after dimensionality reduction by GAPLS had the best accuracy, with R2, RMSE, and RPD of 0.82, 1.94, and 4.55, respectively. The MCC-GAPLS-PLSR model constructed in this study has good inversion potential and is suitable for measuring the chlorophyll content of pepper leaves in the study area, thus promoting efficient cultivation of peppers.

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备注/Memo

备注/Memo:
收稿日期：2023-05-31基金项目：国家重点研发计划项目(2017YFD1100307);贵州省科技支撑计划项目[黔科合支撑(2020)1Y172、黔科合支撑(2021)一般173、黔科合支撑(2021)一般496];贵州省农业科学院青年基金项目[黔农科院青年科技基金(2021)22]作者简介：王宇(1998-),女,贵州安顺人,硕士研究生,主要从事农业摄影测量方面的研究。(E-mail)2029807592@qq.com通讯作者：肖玖军,(E-mail)xiaojiujun0504@163.com
更新日期/Last Update: 2024-07-13