中国农业科技导报2024，Vol.26Issue(5)：110-119,10.DOI:10.13304/j.nykjdb.2022.0977
基于机器学习的油菜叶片水分含量高光谱估测
Hyperspectral Estimation of Rape Leaf Water Content Based on Machine Learning
摘要
Abstract
Leaf water content(LWC)is an important factor affecting the photosynthesis of rapeseed.In order to establish a quantitative monitoring model for LWC with better monitoring effect and universality,rapeseed leaves at the budding and initial flowering stages were selected as the research objects.The leaves were subjected to natural air-drying to remove water,and the mass and spectral information were collected simultaneously.To reduce interference and eliminate noise,5 methods were used to preprocess the spectral data including standard normal variable transformation,Savitzky-Golay convolution smoothing algorithm(SG smoothing),multiple scattering correction,first-order derivative,and second-order derivative,and the optimal preprocessing method was selected by combining with partial least squares(PLS)analysis.Successive projections algorithm(SPA)was used to select sensitive feature wavelengths for water content changes from the preprocessed spectra.Support vector regression(SVR)and back-propagation neural network(BPNN)were used to establish LWC estimation models based on spectral indices using the selected feature wavelengths as independent variables.The results showed that the multiple scattering correction method performed the best,and the correlation coefficients of the prediction sets for both growth stages were above 0.71.SPA selected 6 and 7 feature wavelengths for the budding and initial flowering stages,respectively.In the LWC prediction models for the 2 growth stages,the models based on SVR and BPNN had determination coefficients(R2)above 0.800 for the prediction sets and could achieve accurate monitoring of LWC in rapeseed leaves.The SVR model had better prediction performance than the BPNN model,with R2 values of 0.857 and 0.827 and RMSE values of 1.791 and 1.521,respectively.Therefore,using high-spectral modeling to invert LWC in rapeseed leaves can accurately detect LWC and provide theoretical reference for precision agriculture water management monitoring.关键词
油菜/叶片含水量/高光谱/机器学习Key words
rape/leaf water content/hyperspectral/machine learning分类
农业科技引用本文复制引用
宋丽芳,廖桂平,陈敏,何罗驭阳..基于机器学习的油菜叶片水分含量高光谱估测[J].中国农业科技导报,2024,26(5):110-119,10.基金项目
湖南省现代农业(油菜)产业体建设系项目(湘农发[2019]105号). （油菜）
