利用CARS-BPNN模型的南疆枣园土壤有机质高光谱反演  

Hyperspectral Inversion of Soil Organic Matter in Jujube Orchardin Southern Xinjiang Using CARS-BPNN

作　　者：蔡海辉 周岭[2] 史舟[3] 纪文君 罗德芳 彭杰[1] 冯春晖 CAI Hai-hui;ZHOU Ling;SHI Zhou;JI Wen-jun;LUO De-fang;PENG Jie;FENG Chun-hui(College of Agriculture,Tarim University,Alar 843300,China;College of Mechanical and Electronic Engineering,Tarim University,Alar 843300,China;Institute of Agricultural Remote Sensing and Information Technology Application,Zhejiang University,Hangzhou 310058,China;College of Land Science and Technology,China Agricultural University,Beijing 100083,China;College of Horticulture and Forestry,Tarim University,Alar 843300,China)

机构地区：[1]塔里木大学农学院,新疆阿拉尔843300 [2]塔里木大学机械电气化工程学院,新疆阿拉尔843300 [3]浙江大学农业遥感与信息技术应用研究所,浙江杭州310058 [4]中国农业大学土地科学与技术学院,北京100083 [5]塔里木大学园艺与林学学院,新疆阿拉尔843300

出　　处：《光谱学与光谱分析》2023年第8期2568-2573,共6页Spectroscopy and Spectral Analysis

基　　金：兵团南疆重点产业创新发展支撑计划项目(2020DB003)资助。

摘　　要：土壤有机质(SOM)含量是制定枣园土壤施肥方案的主要依据。合理的施肥方案对提升红枣品质、减少农户投入和增加枣园产出有重要意义。利用传统方法获取枣园SOM含量耗费时间和资源,不符合枣园精准施肥管理的需求,土壤有机质高光谱检测是一种有效的替代方法。为筛选南疆枣园SOM的高光谱快速检测模型,采用网格布点法采集158个枣园土壤样品,测定风干土样的室内高光谱数据和SOM含量。分别将400~2400 nm全波段(R)和通过竞争自适应加权算法(CARS)、连续投影算法(SPA)、粒子群优化算法(PSO)三种数据降维算法筛选的数据集与偏最小二乘回归(PLSR)、反向传播神经网络(BPNN)、卷积神经网络(CNN)三种建模方法结合构建12种枣园SOM含量的组合反演模型,通过对比模型的精度评价指标和训练时间,筛选枣园SOM含量最优光谱反演模型。结果表明:(1)CARS、SPA、PSO三种降维算法都能将光谱数据压缩至原来的10%以下,筛选波长数分别由原来的2001个变量降为98、156、102个,降维组合模型的验证集RPD均大于1.50,均能实现对枣园SOM含量的反演,与R组合模型相比,降维组合模型至少能节省30%的时间成本,特别是与BPNN和CNN等构建的组合模型,能节省90%的训练时间,且模型稳定性更强,模型效果更优。(2)CARS数据集构建组合模型的验证集R^(2)均大于0.85,RPD均大于2.50,RPIQ均大于1.60,在三种降维算法中效果最好;PSO数据集的组合模型验证效果略低于CARS数据集,但优于R数据集,R^(2)均大于0.80、RPD均大于2.00;SPA数据集构建组合模型的验证效果要低于R数据集,在三种降维算法中效果最差。(3)BPNN和CNN两种方法的反演模型验证效果均优于PLSR模型,而在模型训练时间和模型验证效果等方面,BPNN模型优于CNN模型,其结合CARS数据集的验证效果最优,R^(2)为0.91、PRD为3.34、RPIQ为3.17、nRMSE%为11.93,训练时间为58.00 s,模型符合�The soil organic content is the main basis for developing soil fertilization programs in jujube orchards.A reasonable fertilization program is of great significance for improving the quality of jujube,reducing farmers investment and increasing the output of jujube orchards.However,it is time-consuming and resource-intensive to obtain SOM content of jujube orchards using the traditional method,which does not meet the needs of precise fertilization management in jujube orchards.At the same time,the hyperspectral detection of soil organic matter is an effective alternative method.158 soil samples are collected by grid distribution method,and the indoor hyperspectral data and SOM content of air-dried soil samples are determined.The 400~2400 nm full waveband(R)and the datasets selected by three data reduction algorithms of competitive adaptive weighting algorithm(CARS),successive projection algorithm(SPA)and particle swarm optimization algorithm(PSO)are combined with three modeling methods,which are partial least squares regression(PLSR),back propagation neural network(BPNN)and convolutional neural network(CNN)to construct 12 combined inversion models of SOM content of jujube orchards.Moreover,the optimal spectral inversion model of SOM content of jujube orchards was selected by comparing the accuracy evaluation index and training time of the models.The results show that(1)CARS,SPA and PSO can all compress the spectral data to less than 10%of the original data,and the number of screened wavelengths is reduced from the original 2001 variables to 98,156 and 102,respectively.The validation set RPD of the dimensionality reduction combined model are all greater than 1.50,and all of them can achieve the inversion of the SOM content of jujube orchards.Compared with the R combined model,the dimensionality reduction combined model can save at least 30%of time cost,especially the combined model constructed with BPNN and CNN can save 90%of the training time,and the model has stronger stability and better model effect.(2)The vali

关 键 词：枣园土壤有机质 CARS算法 CNN模型 BPNN模型 检测模型 

分 类 号：S153.6[农业科学—土壤学]