Publications

@inproceedings{MarijaTomato2023,

title = {TomatoDIFF: On–plant Tomato Segmentation with Denoising Diffusion Models},

author = {Marija Ivanovska and Vitomir Štruc and Janez Perš },

url = {https://arxiv.org/pdf/2307.01064.pdf

https://ieeexplore.ieee.org/document/10215774},

doi = {10.23919/MVA57639.2023.10215774},

year  = {2023},

date = {2023-07-23},

urldate = {2023-07-23},

booktitle = {18th International Conference on Machine Vision and Applications (MVA 2023)},

pages = {1-6},

abstract = {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. The source code of TomatoDIFF and Tomatopia are available at https://github. com/MIvanovska/TomatoDIFF},

keywords = {agriculture, dataset, deep learning, diffusion, plan segmentation, plant monitoring, robotics, segmentation, tomato dataset},

pubstate = {published},

tppubtype = {inproceedings}

}