南方农业学报2025，Vol.56Issue(1)：97-110,14.DOI:10.3969/j.issn.2095-1191.2025.01.009
基于RGB与高光谱图像的小麦低温胁迫评估模型
Assessment model for low temperature stress in wheat based on RGB and hyperspectral images
摘要
Abstract
[Objective]This study investigated the effects of low temperature stress on chlorophyll fluorescence para-meters,RGB image parameters and hyperspectral indexes of wheat leaves.It aimed to establish an assessment model for wheat under low temperature stress,providing reference for disaster prevention and mitigation in wheat production.[Method]Using winter wheat variety Jimai 22 as the research material,a controlled low temperature stress experiment was conducted during the jointing stage.Three treatments were applied:daytime(8:00-20:00)/nighttime(20:00-8:00 on next day)mean temperatures of 8 ℃/0℃(T1),6 ℃/-2℃(T2)and 4 ℃/-4℃(T3),each lasting for three days.Potted wheat grown under natural field conditions(23 ℃/8℃)served as the control(CK).The changes in chlorophyll fluorescence parameters,RGB image parameters,and hyperspectral indexes of wheat leaves were analyzed on 1,3 and 6 d after the low temperature stress treatments.Wheat low temperature stress assessment models were developed using uni-variate linear regression,random forest(RF)and artificial neural networks(ANN).[Result]The chlorophyll fluores-cence parameter DIo/RC was identified as an effective indicator for assessing wheat low temperature stress.In the univaria-te linear regression model,the model using the enhanced vegetation index(EVI)performed the best,with a regression equation of y=-1.261x+1.401,yielding a coefficient of determination(R2),root mean square error(RMSE),mean abso-lute error(MAE)and mean relative error(MRE)of 0.536,0.058,0.045,and 11.31% respectively.For RF and ANN models,models based on RGB image parameters outperformed those based on hyperspectral indexes.The RF model achieved R2,RMSE,MAE and MRE values of 0.771,0.042,0.033 and 8.57% in the test set,with an R2 improvement of 43.78% and reductions in RMSE,MAE and MRE by 28.31%,28.06% and 24.21% respectively compared to the univaria-te linear regression model.The ANN model achieved R2,RMSE,MAE and MRE values of 0.742,0.046,0.037 and 9.01%,with R2 improving by 38.34% and RMSE,MAE and MRE decreasing by 20.33%,18.06% and 20.32% respec-tively compared to the univariate linear regression model.[Conclusion]The RF model based on RGB image parameters demonstrates the best performance and the highest accuracy,making it suitable for evaluating low temperature stress in wheat.关键词
小麦/叶绿素荧光参数/RGB图像/高光谱图像/低温胁迫评估模型Key words
wheat/chlorophyll fluorescence parameters/RGB image/hyperspectral image/low temperature stress assessment model分类
农业科技引用本文复制引用
余德炤,江晓东,杨莹颖,张建取,忻乐,张艳,秦思容,杨再强..基于RGB与高光谱图像的小麦低温胁迫评估模型[J].南方农业学报,2025,56(1):97-110,14.基金项目
国家重点研发计划项目(2022YFD2300202) National Key Research and Development Program of China(2022YFD2300202) （2022YFD2300202）
