利用CARS-CNN模型的土壤有机质含量高光谱预测  

Hyperspectral Prediction of Soil Organic Matter Content Using CARS-CNN Modelling

作　　者：李浩[1] 于滈 曹永研 郝子源 杨玮[1,2] 李民赞[1,2] LI Hao;YU Hao;CAO Yong-yan;HAO Zi-yuan;YANG Wei;LI Min-zan(Key Lab of Smart Agriculture System,Ministry of Education,China Agricultural University,Beijing 100083,China;Key Lab of Agricultural Information Acquisition Technology,Ministry of Agriculture and Rural Affairs,China Agricultural University,Beijing 100083,China)

机构地区：[1]中国农业大学“智慧农业系统集成研究”教育部重点实验室,北京100083 [2]中国农业大学农业农村部农业信息获取技术重点实验室,北京100083

出　　处：《光谱学与光谱分析》2024年第8期2303-2309,共7页Spectroscopy and Spectral Analysis

基　　金：山东省重点研发计划(重大科技创新工程)项目(2022CXGC020708);国家重点研发计划项目(2019YFE0125500)联合资助。

摘　　要：卷积神经网络(CNN)在数据特征提取方面具有巨大优势,能充分获取数据特征,相较于传统模型具有更好的泛化性。基于CNN开展了土壤有机质(SOM)含量高光谱预测方法及模型研究。以北京市昌平区上庄实验站的320个土壤样本为研究对象,提取可见光-近红外(VIS-NIR)350~1700 nm内的807个光谱波段,通过多元散射校正(MSC)和一阶微分变换进行光谱数据去噪和变换。分别使用连续投影算法(SPA)、竞争性自适应重加权算法(CARS)筛选敏感波长实现光谱数据降维。为解决传统手段泛化性差以及深层CNN网络复杂且负载过大的问题,基于CARS与SPA算法,提出一种基于6层卷积层的浅层CNN模型预测,并对比具有不同卷积尺寸和卷积数量的1D-CNN1、1D-CNN2以寻找最优网络参数。通过对比VGG16、支持向量回归(SVR)、最小二乘回归(PLSR)、随机森林(RF)建立预测模型在特征波长以及全波段的表现确定最佳模型。结果表明,相比于全谱波段和SPA筛选算法,基于CARS筛选特征波长建立的模型整体表现更好,波段数量被压缩至全波段的8%,有效实现了光谱数据的降维。对比全波段数据,基于CARS筛选波长的1D-CNN1、1D-CNN2的表现更好,模型预测R2分别提升了0.028,0.018;RMSE分别降低了0.150和0.107 g·kg^(-1)。整体上,基于CARS的1D-CNN1模型表现最好,预测R2=0.846,RMSE=3.145 g·kg^(-1),降低了网络负载的同时提高了模型精度,同时也证明了小尺寸卷积的表现优于更多数量的大尺寸卷积,能够更好的获取数据特征。通过CARS筛选特征波长结合浅层CNN建立SOM含量预测模型,为建立高精度的SOM含量预测模型提供了方法与参考。Convolutional Neural Network(CNN)has a great advantage in data feature extraction,as it can fully acquire data features and has better generalization than traditional models.This study used a hyperspectral prediction method and modeling of Soil Organic Matter(SOM)content based on CNN.Using 320 soil samples from Shangzhuang Experimental Station,Changping District,Beijing,807 spectral bands within 350~1700 nm in the visible-near-infrared(VIS-NIR)were extracted,and the spectral data were denoised and transformed by the multivariate scattering correction(MSC)and the first-order differential transform.Successive projection algorithm(SPA)and competitive adaptive reweighted Sampling(CARS)were used to screen the sensitive wavelengths to realize the dimensionality reduction of the spectral data,respectively.To solve the problems of poor generalization of traditional means as well as the complexity and overload of deep CNN networks,based on the CARS and SPA algorithms,a shallow CNN model prediction based on 6 convolutional layers is proposed,and 1D-CNN1 and 1D-CNN2 with different convolutional sizes and number of convolutions are compared to find the optimal network parameters.By comparing the performance of VGG16,Support Vector Regression(SVR),Partial Least Squares Regression(PLSR),and Random Forests(RF)to build a prediction model in the feature wavelength and the full waveform.The optimal model was determined.The results show that compared with the full-spectrum band and SPA filtering algorithms,the model based on CARS filtering feature wavelength modeling performs better,and the number of bands is compressed to 8%of the full-wavelength band,which effectively realizes the dimensionality reduction of the spectral data.Comparing the full-band data,1D-CNN1 and 1D-CNN2 based on CARS screening wavelengths performed better,with the model predicted R2 improved by 0.028 and 0.018,respectively,and the RMSE reduced by 0.150 and 0.107 g·kg^(-1),respectively.Overall,the 1D-CNN1 model based on CARS performs the best,with the predict

关 键 词：土壤有机质 卷积神经网络 高光谱 精细农业 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] S153.621[自动化与计算机技术—控制科学与工程]