TomatoDIFF: On-plant Tomato Segmentation with Denoising Diffusion Models
Marija Ivanovska Vitomir Struc Janez Pers
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.