基于无人机多源数据的花生表型估算模型OA

Peanut(Arachis hypogaea L.),a critical oilseed crop,plays a crucial role in ensuring food and oil production security.Accurate,nondestructive,and real-time phenotypic monitoring is essential for optimizing peanut production management.Multispectral data acquired by an unmanned aerial vehicle(UAV)platform during key growth stages were leveraged to extract canopy multispectral(MS),structural(CHM),and textural(TEX)parameters.Four machine learning algorithms,partial least squares regression(PLSR),support vector machine(SVM),artificial neural network(ANN),and random forest regression(RFR),were employed to construct estimation models for plant height,SPAD values,and aboveground biomass.Results demonstrated strong correlations between peanut aboveground biomass/plant height and the near-infrared band(Pearson correlation coefficients were 0.77 and 0.69,respectively).The random forest model,integrating textural,structural,and spectral features,achieved optimal biomass estimation accuracy(R2=0.96).For plant height inversion,the PLSR model combining textural and spectral features performed best(R2=0.94).SPAD estimation using PLSR with fused textural and structural features yielded moderate accuracy(R2=0.39,RMSE=3.06,nRMSE=0.062,RPD=1.30).The research identified feature-specific requirements for machine learning-based estimation of distinct peanut phenotypic traits and established a UAV multi-source data fusion framework capable of accurate,nondestructive,and efficient assessment of plant height and biomass.These findings can provide a robust technical approach for growth monitoring and precision management in peanut cultivation systems.