New Publication: Physics-informed machine learning and Vision Transformer for predicting photosynthetic traits, biomass, and grain yield in winter wheat using UAV multispectral imagery.

Researchers Jingcheng Zhang and Prof. Dr. Kang Yu have developed a physics-informed AI approach to predict photosynthetic traits, biomass, and grain yield in winter wheat using UAV multispectral imagery. By integrating the PROSAIL radiative transfer model, which describes how light interacts with leaves and crop canopies, with Vision Transformer (ViT) deep learning, the study combines physical plant information with spatial patterns captured by drones. The results demonstrate the potential of this integrated approach for accurate, rapid, and non-destructive crop phenotyping, supporting high-throughput wheat breeding and crop monitoring.

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