Early prediction of grain yield helps scientists to make better breeding decisions for wheat. Use of machine learning (ML) methods for fusion of unmanned aerial vehicle (UAV)-based multi-sensor data can improve the prediction accuracy of crop yield. For this, five ML algorithms including Cubist, support vector machine (SVM), deep neural network (DNN), ridge regression (RR) and random forest (RF) were used for multi-sensor data fusion and ensemble learning for grain yield prediction in wheat. A set of thirty wheat cultivars and breeding lines were grown under three irrigation treatments i.e., light, moderate and high irrigation treatments to evaluate the yield prediction capabilities of a low-cost multi-sensor (RGB, multi-spectral and thermal infrared) UAV platform. Multi-sensor data fusion-based yield prediction showed higher accuracy compared to individual-sensor data in each ML model. The coefficient of determination (R²) values for Cubist, SVM, DNN and RR models regarding grain yield prediction were observed from 0.527 to 0.670. Moreover, the results of ensemble learning through integrating the above models illustrated further increase in accuracy. The predictions of ensemble learning showed high R² values up to 0.692, which was higher as compared to individual ML models across the multi-sensor data. Root mean square error (RMSE), residual prediction deviation (RPD) and ratio of prediction performance to inter-quartile range (RPIQ) were calculated to be 0.916 t ha⁻¹, 1.771 and 2.602, respectively. The results proved that low altitude UAV-based multi-sensor data can be used for early grain yield prediction using data fusion and an ensemble learning framework with high accuracy. This high-throughput phenotyping approach is valuable for improving the efficiency of selection in large breeding activities.

谷物产量的早期预测有助于科学家做出更好的小麦育种决策。使用机器学习 (ML) 方法融合基于无人机 (UAV) 的多传感器数据，可以提高作物产量的预测准确性。为此，使用 Cubist、支持向量机 (SVM)、深度神经网络 (DNN)、岭回归 (RR) 和随机森林 (RF) 等五种 ML 算法进行多传感器数据融合和集成学习，用于粮食产量预测。小麦。一组三十个小麦品种和育种系在三种灌溉处理即轻、中、高灌溉处理下种植，以评估低成本多传感器（RGB、多光谱和热红外）无人机平台的产量预测能力. 与每个 ML 模型中的单个传感器数据相比，基于多传感器数据融合的产量预测显示出更高的准确性。决定系数 (R ² ) Cubist、SVM、DNN 和 RR 模型关于谷物产量预测的值从 0.527 观察到 0.670。此外，通过整合上述模型的集成学习结果进一步提高了准确性。集成学习的预测显示R ²值高达 0.692，与跨多传感器数据的单个 ML 模型相比更高。均方根误差 (RMSE)、残差预测偏差 (RPD) 和预测性能与四分位间距的比率 (RPIQ) 计算为 0.916 t ha ^-1，分别为 1.771 和 2.602。结果证明，基于低空无人机的多传感器数据可用于数据融合和集成学习框架的高精度早期粮食产量预测。这种高通量表型分析方法对于提高大型育种活动中的选择效率很有价值。