Research on Chlorophyll SPAD value of Chinese cabbage canopy based on multispectral images from unmanned aerial vehicles
Chlorophyll content can reflect the health status of green vegetables and promote the growth and development of vegetables.As an important vegetable crop,monitoring the growth status of Chinese cabbage is of great significance for improving yield and quality.In this study,nine color features and 24 spectral image combinations were constructed using a multispectral unmanned aerial vehicle,and the SPAD values of Chinese cabbage canopy were obtained simultaneously using a handheld SPAD chlorophyll meter.Four machine learning methods,including partial least squares,support vector regression,BP neural network,and 1D-convolutional neural network,were used to construct the Chinese cabbage SPAD value estimation model.The accuracy of the model was evaluated by the determination coefficient(R2),root mean square error(RMSE),and mean absolute error(MAE).The results showed that the prediction accuracy combining color features,visible light,and multispectral image features was higher than that of a single feature.Among them,the support vector machine-based Chinese cabbage canopy SPAD prediction model showed the highest accuracy with R2=0.785,RMSE=4.320,and MAE=3.451.The conclusion drawn from the comprehensive analysis was that selecting multiple visible light and multispectral image feature combinations as input variables and using the support vector machine model can significantly improve the accuracy of SPAD value estimation,which provids new technical support for rapid and accurate monitoring of Chinese cabbage SPAD values.
NETL
NSTL
万方数据
