农业机械学报2026，Vol.57Issue(12)：213-221,9.DOI:10.6041/j.issn.1000-1298.2026.12.020
基于无人机遥感的冬小麦灌浆期关键酶活性定量反演
Quantitative Inversion of Key Enzyme Activities at Grain Filling Stage of Winter Wheat Based on UAV Remote Sensing
摘要
Abstract
The enzyme activity of flag leaves at the grain filling stage of wheat was a key factor affecting the formation of grain yield and quality.In order to improve the accuracy and efficiency of quantitative inversion of key enzyme activities in winter wheat,a water and fertilizer gradient test of winter wheat was carried out in Henan University of Science and Technology Farm in Ruyang County,Luoyang City,Henan Province from 2023 to 2024,and the test variety was Xinmai 26,and 11 water and nitrogen treatments were set.The remote sensing data were obtained by UAV with RGB camera at the filling stage of winter wheat,the normalized green-red difference index(NGRDI),ratio vegetation index(RVI)and fractional vegetation coverage(FVC)in the visible band were extracted,and the activities of flag leaf nitrate reductase(NR),ribulose 1,5-bisphosphate(RUBP)carboxylase,superoxide dismutase(SOD)and peroxidase(POD)were measured by the laboratory during the same period.Catalase(CAT)activity was used to establish a multivariate stepwise regression model.The results showed that the method had high accuracy in predicting NR activity,POD activity and RUBP carboxylase activity,with determination coefficient(R2)of 0.909,0.770 and 0.683,root mean square error(RMSE)of 3.457 μg/(g·h),1.581 U/g and 0.225 μmol/(g·min),and mean absolute error(MAE)of 1.903 μg/(g·h),1.235 U/g and 0.757 μmol/(g·min),respectively.The results showed that UAV remote sensing had certain potential in the inversion of enzyme activity in winter wheat,and the research results can provide a reference for the inversion of enzyme activity in winter wheat.关键词
冬小麦/酶活性反演/支持向量机/逐步回归模型Key words
winter wheat/enzyme activity inversion/support vector machine(SVM)/stepwise regression model分类
农业科技引用本文复制引用
史喜飞,杨兆临,杜蒙蒙,吉恒善,马超,李春霞..基于无人机遥感的冬小麦灌浆期关键酶活性定量反演[J].农业机械学报,2026,57(12):213-221,9.基金项目
国家重点研发计划项目(2022YFD2300802) （2022YFD2300802）
