摘要
Abstract
Citrus(Citrus reticulata)was selected as the research object,and UAV multispectral data and citrus leaf area index(LAI)data were collected.After band selection and combination of the multispectral data,three feature processing approaches(Boru-ta algorithm,RFECV method,and no feature selection)were employed,each combined with three machine learning regression mod-els(support vector regression(SVR),random forest regression(RFR),and backpropagation neural network regression(BPNNR))to construct nine combined models for LAI estimation.The model parameters were optimized using the GridSearchCV method,the ac-curacy and stability of each model were compared,the optimal LAI prediction model was selected,and a spatial distribution image of citrus LAI was generated.The results showed that the Boruta algorithm could effectively select feature variables and reduce model overfitting.Among the nine combined models,the Boruta_BPNNR model performed best in citrus LAI estimation,exhibiting low data dispersion and a high degree of fit between the regression curve and the diagonal line.The LAI retrieval results indicated that the spa-tial distribution of LAI in the study area showed a distinct north-south gradient difference,with LAI generally higher in the northern re-gion than in the southern region.This was basically consistent with the spatial pattern observed in the field survey,where citrus growth was lush in the north region and relatively sparse in the south region.关键词
柑橘(Citrus reticulata)/无人机多光谱/叶面积指数(LAI)/Boruta_BPNNR模型/反演Key words
citrus(Citrus reticulata)/UAV multispectral/leaf area index(LAI)/Boruta_BPNNR model/retrieval分类
农业科技
