Enhancing rapeseed biomass and yield estimation with ensemble learning and synergistic multidimensional features.

Abstract

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 ( R 2 )=0.72, relative root mean square error (rRMSE)=14.35%; yield: R 2 =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.

Plant phenotyping relevance

UAV画像から抽出した多次元特徴とアンサンブル学習により、ナタネのバイオマス・収量を推定する方法が研究の中心であり、精度評価も実施しているため、植物フェノタイピング手法として適格です。

Code and data availability

The supplied article blocks describe UAV multispectral/RGB imagery, measured biomass/yield data, and ensemble learning models, but contain no data or code availability statement, no public repository, and no author-provided URL. The only pointer is a supplementary materials/methods reference, which is not shown to be a

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