Estimation of Potato SPAD Values Based on Machine Learning and UAV Hyperspectral Remote Sensing
To enable rapid,non-destructive monitoring of the soil plant analysis development(SPAD)value of field-grown potatoes,this research employed unmanned aerial vehicle(UAV)hyperspectral imaging to construct a quantitative detection model during critical growth phases.UAV hyperspectral imagery captured during the tuber initiation and enlargement stages was processed using mathematical transformations.Characteristic bands correlating with the SPAD value were identified using the competitive adaptive reweighted sampling(CARS),uninformative variable elimination(UVE),and random frog(RF)algorithms.Subsequently,partial least squares regression(PLSR),support vector regression(SVR),and back propagation(BP)neural networks were utilized to formulate models for estimating the potato SPAD values.It was observed that the characteristic bands derived from distinct feature selection algorithms varied slightly,with the CARS algorithm demonstrating efficiency in extracting sensitive spectral features,reducing hyperspectral data dimensions,and enhancing model precision.Compared to models constructed with alternative algorithmic combinations,the 1/R-CARS-SVR model displayed superior predictive capabilities,yielding R2 values of 0.88 for the training set and 0.84 for the validation set,and consistent root mean square error(RMSE)values of 0.39 for both.The 1/R-CARS-SVR model was utilized to perform point-by-point SPAD value computations across the study area,and a detailed inversion map was generated.It was found that SPAD value in tuber expansion stage was generally higher than that in tuber formation stage.This map offered a visual representation of potato growth conditions for managerial decision-making,contributing to the theoretical framework and methodological approach for the surveillance of potato growth dynamics.
国家科技期刊平台
NSTL
万方数据
