Artificial intelligence applications enable farmers to optimize crop growth and production while reducing costs and environmental impact. Computer vision-based algorithms in particular, are commonly used for fruit segmentation, enabling in-depth analysis of the harvest quality and accurate yield estimation. In this paper, we propose TomatoDIFF, a novel diffusion-based model for semantic segmentation of on-plant tomatoes. When evaluated against other competitive methods, our model demonstrates state-of-the-art (SOTA) performance, even in challenging environments with highly occluded fruits. Additionally, we introduce Tomatopia, a new, large and challenging dataset of greenhouse tomatoes. The dataset comprises high-resolution RGB-D images and pixel-level annotations of the fruits.

翻译：人工智能应用使农民能够优化作物生长与产量，同时降低成本和环境影响。基于计算机视觉的算法尤其常用于水果分割，可实现对收获质量的深度分析和精准产量估算。本文提出番茄扩散（TomatoDIFF）——一种用于植株番茄语义分割的新型扩散模型。在与其它竞争性方法的对比评估中，即使在高度遮挡果实的困难环境下，该模型仍展现出最先进的性能。此外，我们引入了番茄乐园（Tomatopia）——一个大型且具有挑战性的温室番茄新数据集。该数据集包含高分辨率RGB-D图像及果实的像素级标注。