Orange Quality 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_ | 1866916663837851648 |
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| author | Mekhalfi, Mohamed Lamine Chippendale, Paul Fraile, Francisco Rico, Marcos |
| author_facet | Mekhalfi, Mohamed Lamine Chippendale, Paul Fraile, Francisco Rico, Marcos |
| contents | Orange grading is a crucial step in the fruit industry, as it helps to sort oranges according to different criteria such as size, quality, ripeness, and health condition, ensuring safety for human consumption and better price allocation and client satisfaction. Automated grading enables faster processing, precision, and reduced human labor. In this paper, we implement a deep learning-based solution for orange grading via machine vision. Unlike typical grading systems that analyze fruits from a single view, we capture multiview images of each single orange in order to enable a richer representation. Afterwards, we compose the acquired images into one collage. This enables the analysis of the whole orange skin. We train a convolutional neural network (CNN) on the composed images to grade the oranges into three classes, namely good, bad, and undefined. We also evaluate the performance with two different CNNs (ResNet-18 and SqueezeNet). We show experimentally that multi-view grading is superior to single view grading. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21250 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Orange Quality Grading with Deep Learning Mekhalfi, Mohamed Lamine Chippendale, Paul Fraile, Francisco Rico, Marcos Computer Vision and Pattern Recognition Orange grading is a crucial step in the fruit industry, as it helps to sort oranges according to different criteria such as size, quality, ripeness, and health condition, ensuring safety for human consumption and better price allocation and client satisfaction. Automated grading enables faster processing, precision, and reduced human labor. In this paper, we implement a deep learning-based solution for orange grading via machine vision. Unlike typical grading systems that analyze fruits from a single view, we capture multiview images of each single orange in order to enable a richer representation. Afterwards, we compose the acquired images into one collage. This enables the analysis of the whole orange skin. We train a convolutional neural network (CNN) on the composed images to grade the oranges into three classes, namely good, bad, and undefined. We also evaluate the performance with two different CNNs (ResNet-18 and SqueezeNet). We show experimentally that multi-view grading is superior to single view grading. |
| title | Orange Quality Grading with Deep Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.21250 |