用程序生成番茄图像数据，让模型更准分割温室作物。
Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data
- 用程序建模温室环境，生成多样光照与视角的合成数据
- 在合成数据上微调SAM 3，使文本控制分割更精准
- 适合农业视觉研究者，提升复杂环境下作物分割效果
基于视觉的自动化是减少温室作物生产与表型分析中人工劳动的优秀候选方案，但进展受限于标注训练数据的缺乏。近年来，视觉基础模型在零样本泛化到新领域方面展现出良好前景，但在复杂的农业环境中性能下降。本文提出一种面向番茄植株分割的仿真到真实框架，结合合成数据生成与基础模型微调。我们建模商业化樱桃番茄温室，生成大规模合成数据集，覆盖多样视角、光照条件及植株形态。随后，在合成数据上微调分割一切模型3（SAM 3），使其具备针对温室作物器官的文本条件分割能力，同时保留支持零样本迁移的一般视觉先验。在多个真实温室数据集上的评估表明，结合合成数据与SAM 3微调显著提升了分割性能与模型置信度。为支持社区基准测试，我们公开发布程序化建模模型、生成的合成数据集及微调后的SAM 3权重。
原文摘要 · Abstract (English)
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.
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