草业学报2024，Vol.33Issue(12)：45-58,14.DOI:10.11686/cyxb2024045
无人机多光谱和RGB影像融合的苜蓿产量估测
Estimation of alfalfa yields on the basis of unmanned aerial vehicle multi-spectral and red-green-blue images
摘要
Abstract
Yield is a key component of the economic output of alfalfa(Medicago sativa)pasture.Timely and accurate quantification of alfalfa yield is useful to improve nutrient management and optimize planting patterns.The traditional method to estimate yield in pasture relies on destructive sampling,and there is a certain time lag in obtaining the results.In contrast,unmanned aerial vehicle(UAV)-based monitoring technologies can quickly obtain information to model yield in a non-destructive manner.However,the spectral and spatial resolutions cannot be balanced based on image information from a single sensor,so a comprehensive analysis of crop growth is impossible.It is difficult to effectively improve the accuracy of estimates based on a single UAV image.Therefore,the aim of this study was to explore the potential to combine multi-source image information from UAVs to estimate alfalfa yield during harvesting.In this study,red-green-blue(RGB)and multispectral(MS)images were collected during the alfalfa harvesting period.Based on spectral,texture,and wavelet features extracted from the RGB and MS images,two machine learning algorithms involving partial least squares(PLSR)and Gaussian process regression(GPR)algorithms were used to evaluate the robustness of the alfalfa yield estimation model.The results show that the wavelet features of RGB images were more effective for the comparison of color index and texture features.The combination of the two types of features improved the accuracy of alfalfa yield estimates to some degree.The GPR alfalfa yield estimation model combining three types of features(color index,texture,and wavelet)had high accuracy(training set coefficient of determination R2=0.76,validation set coefficient of determination R2=0.63,and RPD=1.61).For MS images,the model built based on texture features was the most accurate(training set coefficient of determination R2=0.76,validation set coefficient of determination R2=0.63,and the ratio of prediction to deviation RPD=1.61).The alfalfa yield estimation model based on texture features was slightly better than that based on spectral index features,and the GPR alfalfa yield estimation model constructed by combining the two types of features was very accurate(training set coefficient of determination R2=0.83,validation set coefficient of determination R2=0.58,and RPD=1.55).The accuracy of the alfalfa yield estimation model was significantly improved when the RGB image and MS image features were fused.Particularly,the GPR model with three kinds of feature parameters(multi-spectral index,multi-spectral texture,RGB wavelet feature)was the most accurate in estimating alfalfa yield(coefficient of determination R2=0.83 in the training set,coefficient of determination R2=0.75 in the validation set,and RPD=1.98).In conclusion,the GPR algorithm provided the best estimation results,and the estimation accuracy was improved by 13.6%compared with that of the PLSR model.These results provide a reference for remotely monitoring artificial grassland and estimating yield in the future.关键词
苜蓿/无人机影像/产量/植被指数/纹理特征/小波特征/特征融合Key words
alfalfa/drone image/yield/vegetation index/textural features/wavelet characteristic/feature fusion引用本文复制引用
白宇飞,尹航,杨海波,冯振华,李斐..无人机多光谱和RGB影像融合的苜蓿产量估测[J].草业学报,2024,33(12):45-58,14.基金项目
国家重点研发计划项目(2022YFD1900305-03-01)资助. （2022YFD1900305-03-01）
