The RaspGrade Dataset: Towards Automatic Raspberry Ripeness Grading with Deep Learning
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915287048126464 |
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| author | Mekhalfi, Mohamed Lamine Chippendale, Paul Poiesi, Fabio Bonecher, Samuele Osler, Gilberto Zancanella, Nicola |
| author_facet | Mekhalfi, Mohamed Lamine Chippendale, Paul Poiesi, Fabio Bonecher, Samuele Osler, Gilberto Zancanella, Nicola |
| contents | This research investigates the application of computer vision for rapid, accurate, and non-invasive food quality assessment, focusing on the novel challenge of real-time raspberry grading into five distinct classes within an industrial environment as the fruits move along a conveyor belt. To address this, a dedicated dataset of raspberries, namely RaspGrade, was acquired and meticulously annotated. Instance segmentation experiments revealed that accurate fruit-level masks can be obtained; however, the classification of certain raspberry grades presents challenges due to color similarities and occlusion, while others are more readily distinguishable based on color. The acquired and annotated RaspGrade dataset is accessible on Hugging Face at: https://huggingface.co/datasets/FBK-TeV/RaspGrade. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08537 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The RaspGrade Dataset: Towards Automatic Raspberry Ripeness Grading with Deep Learning Mekhalfi, Mohamed Lamine Chippendale, Paul Poiesi, Fabio Bonecher, Samuele Osler, Gilberto Zancanella, Nicola Computer Vision and Pattern Recognition This research investigates the application of computer vision for rapid, accurate, and non-invasive food quality assessment, focusing on the novel challenge of real-time raspberry grading into five distinct classes within an industrial environment as the fruits move along a conveyor belt. To address this, a dedicated dataset of raspberries, namely RaspGrade, was acquired and meticulously annotated. Instance segmentation experiments revealed that accurate fruit-level masks can be obtained; however, the classification of certain raspberry grades presents challenges due to color similarities and occlusion, while others are more readily distinguishable based on color. The acquired and annotated RaspGrade dataset is accessible on Hugging Face at: https://huggingface.co/datasets/FBK-TeV/RaspGrade. |
| title | The RaspGrade Dataset: Towards Automatic Raspberry Ripeness Grading with Deep Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.08537 |