mlsanityData to 14 Oct 2026

TomatoDIFF: On-plant Tomato Segmentation with Denoising Diffusion Models

arXiv 2307.01064 ↗First posted 3 Jul 2023cs.CVcited by 1

On the map: Diffusion models → Inverse problems and image restoration

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. Builds on 24 of its 47 references that are in the map. First posted on arXiv 3 Jul 2023.

Builds on

24 of 47 references.

References · 24

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12 more
  1. 2022Diffusion Models in Vision: A Surveycited by 1,015
  2. 2019YOLACT: Real-time Instance Segmentationcited by 443
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  5. 2020BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentationcited by 294
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Citing papers per year: 2023: 1.

1 paper.

  1. 2023ResEnsemble-DDPM: Residual Denoising Diffusion Probabilistic Models for Ensemble Learningcited by 0

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  1. 2026Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data
  2. 2024SynthSet: Generative Diffusion Model for Semantic Segmentation in Precision Agriculture
  3. 2025Automated Tomato Maturity Estimation Using an Optimized Residual Model with Pruning and Quantization Techniques
  4. 2026SPROUT: A Scalable Diffusion Foundation Model for Multi-Crop Plant Phenotyping
  5. 2025Zero-shot Hierarchical Plant Segmentation via Foundation Segmentation Models and Text-to-image Attention
  6. 2026Perturbation-Aware Diffusion-Guided Hybrid Segmentation for Robust and Annotation-Efficient Plant Stress Phenotyping
  7. 2026Selective Spatio-Temporal Fruit Inspector (S2FI): A Mamba-based Framework for Rotational Fruit Quality Assessment
  8. 2022Development and evaluation of automated localisation and reconstruction of all fruits on tomato plants in a greenhouse based on multi-view perception and 3D multi-object tracking