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Enhancing rapeseed biomass and yield estimation with ensemble learning and synergistic multidimensional features 认领 引用 被引量：1
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作者 Yanni ZHANG Xiaoyu CHAI +2 位作者 Jinpeng HU Yaxiao NIU Lizhang XU 《Journal of Zhejiang University-SCIENCE B》 SCIE CAS CSCD 2026年第5期499-516,I0007-I0011,共18页
Accurate rapeseed yield and biomass estimation at the meter scale prior to harvest is crucial for precision harvesting.However,there is a scarcity of structured research on the estimation of rapeseed biomass yield.Thi... Accurate rapeseed yield and biomass estimation at the meter scale prior to harvest is crucial for precision harvesting.However,there is a scarcity of structured research on the estimation of rapeseed biomass yield.This study aims to address this gap by focusing on rapeseed in Jiangsu Province.Multispectral and RGB images captured by unmanned aerial vehicles(UAVs)were taken during key growth stages(budding,flowering,and podding stages).Using the extracted multidimensional features,we developed biomass-yield estimation models using four machine learning techniques.Subsequently,we employed ensemble learning with multidimensional,multi-stage data and used Shapley additive explanation(SHAP)for feature contribution analysis,thereby constructing a framework for predicting rapeseed harvest characteristics with high estimation accuracy and interpretability.Our analysis indicates that spectral‒texture is the most effective feature combination for biomass estimation,whereas the optimal combination for yield estimation includes three-dimensional(3D)spectral‒textural‒structural features.The synergy of these features,coupled with an ensemble learning model,significantly enhanced the accuracy of rapeseed biomass-yield estimation(biomass:coefficient of determination(R2)=0.72,relative root mean square error(rRMSE)=14.35%;yield:R2=0.68,rRMSE=13.67%).The proposed model also achieved stable prediction results across the variety‒density interaction.Overall,this study presents an accurate and generalizable approach for estimating rapeseed biomass yield across various planting patterns,offering new insights for precision harvesting. 展开更多
关键词 Ensemble learning Decision-making Feature synergy Temporal fit Planting pattern
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Estimation of transpiration coefficient and aboveground biomass in maize using time-series UAV multispectral imagery 认领 引用 被引量：6
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作者 Guomin Shao Wenting Han +5 位作者 Huihui Zhang Yi Wang Liyuan Zhang Yaxiao Niu Yu Zhang Pei Cao 《The Crop Journal》 SCIE CSCD 2022年第5期1376-1385,共10页
Estimating spatial variation in crop transpiration coefficients(CTc) and aboveground biomass(AGB)rapidly and accurately by remote sensing can facilitate precision irrigation management in semiarid regions. This study ... Estimating spatial variation in crop transpiration coefficients(CTc) and aboveground biomass(AGB)rapidly and accurately by remote sensing can facilitate precision irrigation management in semiarid regions. This study developed and assessed a novel machine learning(ML) method for estimating CTc and AGB using time-series unmanned aerial vehicle(UAV)-based multispectral vegetation indices(VIs)of maize under several irrigation treatments at the field scale. Four ML regression methods: multiple linear regression(MLR), support vector regression(SVR), random forest regression(RFR), and adaptive boosting regression(ABR), were used to address the complex relationship between CTcand VIs. AGB was then estimated using exponential, logistic, sigmoid, and linear equations because of their clear mathematical formulations based on the optimal CTcestimation model. The UAV VIs-derived CTcusing the RFR estimation model yielded the highest accuracy(R2= 0.91, RMSE = 0.0526, and n RMSE = 9.07%). The normalized difference red-edge index, transformed chlorophyll absorption in reflectance index, and simple ratio contributed significantly to the RFR-based CTcmodel. The accuracy of AGB estimation using nonlinear methods was higher than that using the linear method. The exponential method yielded the highest accuracy(R2= 0.76, RMSE = 282.8 g m, and n RMSE = 39.24%) in both the 2018 and 2019 growing seasons. The study confirms that AGB estimation models based on cumulative CTcperformed well under several irrigation treatments using high-resolution time-series UAV multispectral VIs and can support irrigation management with high spatial precision at a field scale. 展开更多
关键词 Crop transpiration Normalized difference red-edge index Unmanned aerial vehicles Random forest regression Biomass
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