R. Roscher

By effectively predicting unobserved plant structures, SG-AMP enhances active perception and motion planning, achieving notable improvements in semantic accuracy and depth estimation.

Real-world datasets reveal that tracking tomato ripeness can significantly enhance agricultural automation and phenotyping techniques.