Enhancing model performance through date fusion in multispectral and RGB image-based field phenotyping of wheat grain yield
Paul Heinemann, Lukas Prey, Anja Hanemann, Ludwig Ramgraber, Johannes Seidl-Schulz, Patrick Noack
Abstract
Abstract This study assessed the potential of multispectral (MS) and RGB imagery acquired by unmanned aerial vehicles (UAVs) for predicting wheat grain yield (GY) in field trials. We investigated the influence of single-date measurements and optimal multi-date strategies across diverse locations and years in southeast and eastern Germany. UAV-based MS and RGB data were collected throughout the growing season, and machine learning models were developed using vegetation indices to predict GY. Results revealed that the accuracy of single-date predictions varied, with later stages, especially grain-filling, demonstrating higher performance. Combining data from multiple dates improved GY prediction, and especially the inclusion of June acquisitions (likewise around grain-filling phase) consistently enhances the prediction accuracy of multi-date models. However, the optimal combination of dates was found to be year- and location-specific. Overall, MS data slightly outperformed RGB data, although RGB showed advantages in specific instances, particularly during later growth stages. This study provides valuable insights for optimizing UAV-based phenotyping in wheat breeding trials, highlighting the importance of strategic measurement timing and sensor selection for accurate GY prediction.