Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data
Researchers introduce a sim-to-real framework for tomato plant segmentation that leverages synthetic data generation and fine-tuning of the Segment Anything Model 3 (SAM 3). By procedurally modeling a commercial cherry tomato greenhouse, they create a large-scale synthetic dataset to specialize SAM 3's text-conditioned segmentation for greenhouse crop organs. The method leads to notable improvements in segmentation performance and model confidence on real-world greenhouse datasets.
Why it matters: This work provides a practical solution to the scarcity of annotated training data in agricultural computer vision, advancing reliable automation for crop phenotyping.
Full story at: arXiv Computer Vision ↗