农业机械学报2025，Vol.56Issue(5)：103-110,8.DOI:10.6041/j.issn.1000-1298.2025.05.010
无人机冠层3D时序动态建模驱动棉花生物量高精度反演研究
UAV-driven 3D Spatiotemporal Canopy Modeling Enhanced High-accuracy Cotton Biomass Retrieval
摘要
Abstract
Accurate above ground biomass(AGB)estimation is a key technology for crop growth monitoring and precision agriculture decision making.Aiming to address the two limitations of traditional unmanned aerial vehicle(UAV)remote sensing methods in cotton AGB estimation-models based on vegetation indices(VIs)were susceptible to the interference of canopy spectral saturation effects,and it was difficult to quantify the spatio-temporal heterogeneity of the dynamics of three-dimensional canopy structure and AGB accumulation-the spatial analysis of three-dimensional UAV point clouds and the temporal characteristics of canopy cover were integrated to construct a multi-dimensional estimation model based on plant height × canopy cover(PH × CC).By designing a comparative experimental framework,the performance differences between the PH × CC model and four types of traditional models were investigated:VIs combined with random forest(RF),gradient boosting(GB),support vector machine(SVM)and backpropagation neural network(BPNN)were systematically evaluated.The results showed that the PH × CC model had significant advantages on the test set.Its coefficient of determination of estimation accuracy(R2)was 0.93,and the root mean square error(RMSE)was 15.30 g/m2,which was an improvement of 22.3％compared with that of the optimal traditional model(RF:R2=0.76,RMSE was 23.35 g/m2)(P＜0.01).The mechanism analysis showed that the PH × CC parameters can analyze 83％of the variation in canopy structure(only 57％for the traditional VIs model)by synergistically representing the dynamic coupling relationship between the vertical expansion of PH and the horizontal expansion of canopy width,significantly improving the model's ability to explain the interaction mechanism between AGB and structure.The research result can provide a method to overcome the technical bottleneck of"spectral-structural"information fusion in UAV agricultural situation monitoring,and at the same time it can provide a quantifiable modelling tool to analyze the biological mechanism of cotton canopy growth dynamics.关键词
棉花/地上部生物量/植被指数/3D模型/机器学习算法Key words
cotton/aboveground biomass/vegetation index/3D model/machine learning algorithm分类
农业科技引用本文复制引用
胡正东,汤秋香,樊世语,鲍龙龙,古丽达娜·沙勒山,林涛..无人机冠层3D时序动态建模驱动棉花生物量高精度反演研究[J].农业机械学报,2025,56(5):103-110,8.基金项目
新疆维吾尔自治区重点研发计划项目(2024B02004)、国家棉花产业技术体系项目(CARS-15-12)、国家重点研发计划项目(2024YFD2300604)、中央引导地方项目(ZYYD2024CG23)、新疆农业科学院农业科技创新稳定支持计划项目(xjnkywdzc-2023007)、新疆"天山英才"计划"青年拔尖人才"项目和新疆"天山英才"计划"棉花轻简高效栽培技术创新团队"项目(2023TSYCTD004) （2024B02004）
