Jul 20, 2026 · cs.CVJ/K move · Enter open · S save
Samy Mounir, Mikolaj Cieslak, Najmeddine Dhieb, Hakim Ghazzai+7
Computer, Electrical and Mathematical Sciences and Engineering Division, KAUST, KSA · Department of Computer Science, University of Calgary, Canada · Department of Computer Science, Kiel University, Germany · Department of Artificial Intelligence, Adam Mickiewicz University, Poland · Department of Electronics and Telecommunications, Polytechnic University of Turin, Italy
Vision-based automation is an excellent candidate for reducing manual labor in greenhouse crop production and phenotyping. However, progress is constrained by the lack of annotated training data. Recent advances in vision-based foundational models have shown promising results in zero-shot generalization to novel domains, but their performance drops in complex agricultural environments. In this work, we present a sim-to-real framework for tomato plant segmentation that combines synthetic data generation with fine-tuning of a foundation model. We model a commercial cherry tomato greenhouse and use it to generate a large-scale synthetic dataset under diverse viewpoints, lighting conditions, and plant morphology. Subsequently, we fine-tune the Segment Anything Model 3 (SAM 3) on the synthetic dataset, specializing its text-conditioned segmentation behavior for greenhouse crop organs while retaining the general visual prior that makes zero-shot transfer possible. By evaluating our framework on multiple real-world greenhouse datasets, we demonstrate that combining synthetic data with SAM 3 fine-tuning significantly improves segmentation performance and model confidence. To support community benchmarking, we publicly release the procedural model, the generated synthetic dataset, and our fine-tuned SAM 3 weights.