Accurately and economically estimated crop above-ground biomass (AGB) and bean yield (BY) are critical for cultivation management in precision agriculture. Unmanned aerial vehicle (UAV) platforms have shown great potential in crop AGB and BY estimation due to their ability to rapidly acquire remote sensing data with high temporal–spatial resolution. In this study, a low-cost and consumer-grade camera mounted on a UAV was adopted to acquire red–green–blue (RGB) images, which were then combined with ensemble learning to estimate faba bean AGB and BY. The following results were obtained: (1) The faba bean plant height derived from UAV RGB images presented a strong correlation with the ground measurement (R² = 0.84, RMSE = 63.6 mm). (2) The accuracy of BY estimation (R² = 0.784, RMSE = 0.460 t ha⁻¹, NRMSE = 14.973%) based on RGB images was higher than the accuracy of AGB estimation (R² = 0.618, RMSE = 0.606 t ha⁻¹, NRMSE = 16.746%). (3) The combination of three variables (vegetation index, structural information, textural information) improved the AGB and BY estimation accuracy. (4) The AGB and BY estimation performance were best for the mid bean-filling stage. (5) The ensemble learning model provided higher AGB and BY estimation accuracy than the five base learners (k-nearest neighbor, support vector machine, ridge regression, random forest and elastic net models). These results indicate that UAV RGB images combined with machine learning algorithms, particularly ensemble learning models, can provide relatively accurate faba bean AGB (R² = 0.683, RMSE = 0.568 t ha⁻¹, NRMSE = 15.684%) and BY (R² = 0.854, RMSE = 0.390 t ha⁻¹, NRMSE = 12.693%) estimation and considerably contribute to the high-throughput phenotyping study of food legumes.

准确且经济地估算作物地上生物量 (AGB) 和豆类产量 (BY) 对于精准农业的栽培管理至关重要。无人机 (UAV) 平台在作物 AGB 和 BY 估计方面显示出巨大潜力，因为它们能够快速获取具有高时空分辨率的遥感数据。在这项研究中，采用安装在无人机上的低成本消费级相机来获取红-绿-蓝 (RGB) 图像，然后将其与集成学习相结合以估计蚕豆 AGB 和 BY。得到以下结果： (1) 从无人机RGB 图像中获取的蚕豆株高与地面测量值具有很强的相关性（R ²  = 0.84，RMSE = 63.6 mm）。(2) BY估计的准确性(R ² = 0.784, RMSE = 0.460 t ha ⁻¹ , NRMSE = 14.973%) 基于 RGB 图像的精度高于 AGB 估计的精度 (R ²  = 0.618, RMSE = 0.606 t ha ⁻¹, NRMSE = 16.746%)。(3)三个变量(植被指数、结构信息、纹理信息)的结合提高了AGB和BY的估计精度。(4) AGB 和 BY 估计性能在豆浆中期最好。(5) 集成学习模型提供了比五个基础学习器（k-最近邻、支持向量机、岭回归、随机森林和弹性网络模型）更高的 AGB 和 BY 估计精度。这些结果表明，无人机 RGB 图像结合机器学习算法，特别是集成学习模型，可以提供相对准确的蚕豆 AGB（R ²  = 0.683，RMSE = 0.568 t ha ⁻¹，NRMSE = 15.684%）和 BY（R ²  = 0.854, RMSE = 0.390 吨公顷⁻¹, NRMSE = 12.693%) 估计，并极大地促进了食品豆类的高通量表型研究。