Intelligent detection of chlorophyll content in kiwifruit leaves based on hyperspectroscopy
To accurately and precisely analyze the growth and health status of kiwifruit trees,a leaf hyperspectral dataset was constructed using kiwifruit trees in the Guanzhong Plain of Shaanxi Province.The dataset was divided based on the stochastic method and the Kennard-Stone method,and the characteristic bands of the samples were extracted using the competitive adaptive reweighted sampling(CARS),principal component analysis(PCA),and iteratively retains informative variables(IRIV)algorithms.Multiple linear regression(MLR),ridge regression(RR),partial least squares regression(PLSR),support vector regression(SVR)and random forest regression(RFR)were used to establish an intelligent detection model for leaf chlorophyll content.Comparative analysis of the models showed that the CARS-RR model based on the 81 feature bands extracted by the CARS algorithm had the best prediction effect,with an R2 of 0.86 and an RMSE of 2.71 on the validation set.Therefore,the proposed intelligent detection model can detect the chlorophyll content of kiwifruit trees based on the spectral information in a nondestructive manner.Furthermore,it can analyze the overall health status of the orchard,providing decision-making support for subsequent refined orchard management.
万方数据
维普
