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The pre-harvest estimation of seed composition from standing crops is imperative for field management practices and plant phenotyping. This paper presents for the first time the potential of Unmanned Aerial Vehicles (UAV)-based high-resolution hyperspectral and LiDAR data acquired from in-season stand crops for estimating seed protein and oil compositions of soybean and corn using multisensory data fusion and automated machine learning. UAV-based hyperspectral and LiDAR data was collected during the growing season (reproductive stage five (R5)) of 2020 over a soybean test site near Columbia, Missouri and a cornfield at Urbana, Illinois, USA. Canopy spectral and texture features were extracted from hyperspectral imagery, and canopy structure features were derived from LiDAR point clouds. The extracted features were then used as input variables for automated machine-learning methods available with the H2O Automated Machine-Learning framework (H2O-AutoML). The results presented that: (1) UAV hyperspectral imagery can successfully predict both the protein and oil of soybean and corn with moderate accuracies; (2) canopy structure features derived from LiDAR point clouds yielded slightly poorer estimates of crop-seed composition compared to the hyperspectral data; (3) regardless of machine-learning methods, the combination of hyperspectral and LiDAR data outperformed the predictions using a single sensor alone, with an R2 of 0.79 and 0.67 for corn protein and oil and R2 of 0.64 and 0.56 for soybean protein and oil; and (4) the H2O-AutoML framework was found to be an efficient strategy for machine-learning-based data-driven model building. Among the specific regression methods evaluated in this study, the Gradient Boosting Machine (GBM) and Deep Neural Network (NN) exhibited superior performance to other methods. This study reveals opportunities and limitations for multisensory UAV data fusion and automated machine learning in estimating crop-seed composition. [ABSTRACT FROM AUTHOR]