mlsanityData to 14 Oct 2026

SemanticSugarBeets: A Multi-Task Framework and Dataset for Inspecting Harvest and Storage Characteristics of Sugar Beets

arXiv 2504.16684 ↗First posted 23 Apr 2025cs.CV cs.LGcited by 0

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Abstract (arXiv)

While sugar beets are stored prior to processing, they lose sugar due to factors such as microorganisms present in adherent soil and excess vegetation. Their automated visual inspection promises to aide in quality assurance and thereby increase efficiency throughout the processing chain of sugar production. In this work, we present a novel high-quality annotated dataset and two-stage method for the detection, semantic segmentation and mass estimation of post-harvest and post-storage sugar beets in monocular RGB images. We conduct extensive ablation experiments for the detection of sugar beets and their fine-grained semantic segmentation regarding damages, rot, soil adhesion and excess vegetation. For these tasks, we evaluate multiple image sizes, model architectures and encoders, as well as the influence of environmental conditions. Our experiments show an mAP50-95 of 98.8 for sugar-beet detection and an mIoU of 64.0 for the best-performing segmentation model.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 42 references that are in the map. First posted on arXiv 23 Apr 2025.

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9 of 42 references.

References · 9

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  3. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
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  6. 2017Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentationscited by 620
  7. 2016Using Deep Learning for Image-Based Plant Disease Detectioncited by 219
  8. 2020Multi-Attention-Network for Semantic Segmentation of Fine Resolution Remote Sensing Imagescited by 59
  9. 2023PhenoBench -- A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domaincited by 31

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