土壤2023，Vol.55Issue(6)：1347-1353,7.DOI:10.13758/j.cnki.tr.2023.06.023
基于1D-CNN的土壤全氮近红外光谱预测模型
Near-infrared Spectral Prediction Model of Soil Total Nitrogen Based on 1D-CNN
摘要
Abstract
A total of 410 soil samples were collected in Wuxi,Jiangsu,China,and total nitrogen contents and soil sample spectra were analyzed indoors.The spectral data underwent preprocessing,including mean centering,standard normal variate transformation,and trend correction.Regression prediction models for soil total nitrogen content were established using partial least squares(PLS),back propagation(BP)neural networks,and one-dimensional convolutional neural networks(1D-CNN).Each model underwent ten-fold cross-validation using datasets preprocessed with various methods,and the average values of the coefficient of determination(R2)and root mean square error(RMSE)were recorded to compare the impact of these three preprocessing methods on model accuracy.The results demonstrated the reliability of the 1D-CNN model constructed based on soil near-infrared spectral data.The R2 values for the 1D-CNN model trained with raw data and data preprocessed with mean centering,standard normal variate transformation,and trend correction were 0.907,0.931,0.922,and 0.964,respectively.In comparison,the R2 values for the PLS model were 0.856,0.863,0.861,and 0.880,while the BP neural network model's R2 values were 0.874,0.907,0.901,and 0.911.The 1D-CNN model consistently outperformed the PLS and BP neural network models on both raw and preprocessed spectral data.Preprocessing the spectral data effectively enhanced the 1D-CNN model's performance,with trend correction demonstrating the most substantial improvement.Hence,1D-CNN is adept at extracting spectral features and establishing a robust mapping relationship with nitrogen content,effectively preventing overfitting.Even with unprocessed spectral data,it still achieves a commendable level of accuracy.关键词
近红外光谱/全氮含量/光谱预处理/1D-CNNKey words
Near infrared spectroscopy/Total nitrogen content/Spectral pre-processing/1D-CNN分类
农业科技引用本文复制引用
秦文虎,董凯月,邓志超..基于1D-CNN的土壤全氮近红外光谱预测模型[J].土壤,2023,55(6):1347-1353,7.基金项目
江苏省重点研发计划项目(BE2019311)资助. （BE2019311）
