基于小波变换和VCPA-GA算法的人参果叶片叶绿素含量高光谱估算  

Hyperspectral Estimation of Chlorophyll Content in Ginseng Fruit Leaves Based on Wavelet Transform and VCPA-GA Algorithm

作　　者：郭金锋 张志从 吾木提·艾山江 周忠晔 续文宇 玉苏甫·艾海买江 Guo Jinfeng;Zhang Zhicong;Umut Hasan;Zhou Zhongye;Xu Wenyu;Yusup Ahmat(College of Resources and Environment,Yili Normal University,Yining 835000,China;Institute of Resources and Ecology,Yili Normal University,Yining 835000,China;School of Geographic Sciences,East China Normal University,Shanghai 200241,China)

机构地区：[1]伊犁师范大学资源与环境学院,新疆伊宁835000 [2]伊犁师范大学资源与生态研究所,新疆伊宁835000 [3]华东师范大学地理科学学院,上海200241

出　　处：《热带地理》2025年第3期514-526,共13页Tropical Geography

基　　金：伊犁师范大学科研项目(2022YSYY003);伊犁哈萨克自治州科技计划项目(YJC2024A05);第三次新疆综合科学考察(2022xjkk20220405)。

摘　　要：叶片叶绿素含量(Leaf Chlorophyll Contents,LCCs)作为植物重要的生理生化参数之一,其含量的变化直接或间接影响植物的生长发育。通过使用高光谱遥感技术对人参果LCC进行快速无损监测,有利于实现精准农业的发展。文章以人参果叶片高光谱数据和对应的人参果LCC为数据集,使用离散小波变换(Discrete Wavelet Transform,DWT)算法,提取人参果叶片高光谱数据0~10层低频小波系数,将0~10层光谱数据集与对应的人参果LCC进行Pearson相关性分析,然后将变量组合集群分析(Variable Combination Population Analysis,VCPA)与遗传算法(Genetic Algorithm,GA)结合,使用VCPA-GA算法提取人参果全谱和各分解层敏感波段,通过4种机器学习模型构建人参果LCC的估测模型。结果表明,DWT能提高人参果LCC的预测性能,在4种机器学习模型中,4层BP-AdaBoost模型的预测性能最好,R^(2)达到0.919,MAPE=2.090%,RMSE=1.453,RPD=3.900,其次PSO-BPNN回归模型的预测性能也表现出较高的准确性。文章表明,人参果高光谱数据经DWTVCPA-GA算法处理后,使用4层低频小波系数重组的光谱数据构建BP-AdaBoost回归预测模型时对人参果LCC的估算性能最好。Leaf Chlorophyll Content(LCC)is vital for both direct and indirect plant growth and development.Accurate monitoring of LCC in ginseng fruits provides essential data for assessing their photosynthetic and nutritional status,which is beneficial for the development of precision agriculture.Traditional chemical analyses in laboratories require a large number of samples,which are not only time-consuming and destructive,but also fail to meet the precise management needs of extensive fields.Although some handheld devices can measure the leaf LCC accurately and quickly without causing damage,they cannot provide large-scale information.Hyperspectral remote sensing is widely applied for rapid and non-destructive LCC monitoring because of its strong continuity and abundant data.In this study,we used ginseng fruit leaf hyperspectral data and the corresponding LCCs as datasets.We applied the Discrete Wavelet Transform(DWT)to extract the low-frequency coefficients from the 0-10 layers of the hyperspectral data.We then conducted a Pearson correlation analysis on the 0-10 layer spectral datasets and their corresponding LCCs.We combined Variable Combination Pattern Analysis(VCPA)with Genetic Algorithm(GA),employing the combined VCPA-GA algorithm to extract sensitive bands from the full spectrum and each decomposed layer of the ginseng fruit leaf.Finally,we established estimation models for the ginseng fruit LCC using the Back Propagation Neural Network(BPNN),GA-BPNN,Particle Swarm Optimization(PSO)-BPNN,and BP-AdaBoost neural network models.Among the four machine-learning models,the BP-AdaBoost neural network exhibited the best overall predictive performance.The predictive performance of the PSO-BPNN model was similar to that of the BPNN model,whereas the GA-BPNN model exhibited the lowest predictive performance.This study shows:(1)The 1-5 layer DWT spectra accurately reflect the overall characteristics of the original spectrum,with a decrease in correlation at each layer beyond the fifth layer,and the spectra beyond the seventh

关 键 词：离散小波变换 混合变量选择算法 深度学习 叶片叶绿素含量 人参果 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置] S667.9[自动化与计算机技术—控制科学与工程]