Automatic Monitoring Cheese Ripeness Using Computer Vision and Artificial Intelligence
Ripening is a very important process that contributes to cheese quality, as its characteristics are determined by the biochemical changes that occur during this period. Therefore, monitoring ripening time is a fundamental task to market a quality product in a timely manner. However, it is difficult...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9956763/ |
| _version_ | 1811300653233864704 |
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| author | Andrea Loddo Cecilia Di Ruberto Giuliano Armano Andrea Manconi |
| author_facet | Andrea Loddo Cecilia Di Ruberto Giuliano Armano Andrea Manconi |
| author_sort | Andrea Loddo |
| collection | DOAJ |
| description | Ripening is a very important process that contributes to cheese quality, as its characteristics are determined by the biochemical changes that occur during this period. Therefore, monitoring ripening time is a fundamental task to market a quality product in a timely manner. However, it is difficult to accurately determine the degree of cheese ripeness. Although some scientific methods have also been proposed in the literature, the conventional methods adopted in dairy industries are typically based on visual and weight control. This study proposes a novel approach aimed at automatically monitoring the cheese ripening based on the analysis of cheese images acquired by a photo camera. Both computer vision and machine learning techniques have been used to deal with this task. The study is based on a dataset of 195 images (specifically collected from an Italian dairy industry), which represent Pecorino cheese forms at four degrees of ripeness. All stages but the one labeled as “day 18”, which has 45 images, consist of 50 images. These images have been handled with image processing techniques and then classified according to the degree of ripening, i.e., 18, 22, 24, and 30 days. A 5-fold cross-validation strategy was used to empirically evaluate the performance of the models. During this phase, each training fold was augmented online. This strategy allowed to use 624 images for training, leaving 39 original images per fold for testing. Experimental results have demonstrated the validity of the approach, showing good performance for most of the trained models. |
| first_indexed | 2024-04-13T06:54:30Z |
| format | Article |
| id | doaj.art-c9cb94d587e04fbb9cc640f1fcdb466e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T06:54:30Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c9cb94d587e04fbb9cc640f1fcdb466e2022-12-22T02:57:18ZengIEEEIEEE Access2169-35362022-01-011012261212262610.1109/ACCESS.2022.32237109956763Automatic Monitoring Cheese Ripeness Using Computer Vision and Artificial IntelligenceAndrea Loddo0https://orcid.org/0000-0002-6571-3816Cecilia Di Ruberto1https://orcid.org/0000-0003-4641-0307Giuliano Armano2Andrea Manconi3https://orcid.org/0000-0002-1287-2768Department of Mathematics and Computer Science, University of Cagliari, Cagliari, ItalyDepartment of Mathematics and Computer Science, University of Cagliari, Cagliari, ItalyDepartment of Mathematics and Computer Science, University of Cagliari, Cagliari, ItalyInstitute of Biomedical Technologies, National Research Council, CNR, Segrate, Milan, ItalyRipening is a very important process that contributes to cheese quality, as its characteristics are determined by the biochemical changes that occur during this period. Therefore, monitoring ripening time is a fundamental task to market a quality product in a timely manner. However, it is difficult to accurately determine the degree of cheese ripeness. Although some scientific methods have also been proposed in the literature, the conventional methods adopted in dairy industries are typically based on visual and weight control. This study proposes a novel approach aimed at automatically monitoring the cheese ripening based on the analysis of cheese images acquired by a photo camera. Both computer vision and machine learning techniques have been used to deal with this task. The study is based on a dataset of 195 images (specifically collected from an Italian dairy industry), which represent Pecorino cheese forms at four degrees of ripeness. All stages but the one labeled as “day 18”, which has 45 images, consist of 50 images. These images have been handled with image processing techniques and then classified according to the degree of ripening, i.e., 18, 22, 24, and 30 days. A 5-fold cross-validation strategy was used to empirically evaluate the performance of the models. During this phase, each training fold was augmented online. This strategy allowed to use 624 images for training, leaving 39 original images per fold for testing. Experimental results have demonstrated the validity of the approach, showing good performance for most of the trained models.https://ieeexplore.ieee.org/document/9956763/Cheese ripeningimage analysisimage processingmachine learningimage classificationdeep learning |
| spellingShingle | Andrea Loddo Cecilia Di Ruberto Giuliano Armano Andrea Manconi Automatic Monitoring Cheese Ripeness Using Computer Vision and Artificial Intelligence IEEE Access Cheese ripening image analysis image processing machine learning image classification deep learning |
| title | Automatic Monitoring Cheese Ripeness Using Computer Vision and Artificial Intelligence |
| title_full | Automatic Monitoring Cheese Ripeness Using Computer Vision and Artificial Intelligence |
| title_fullStr | Automatic Monitoring Cheese Ripeness Using Computer Vision and Artificial Intelligence |
| title_full_unstemmed | Automatic Monitoring Cheese Ripeness Using Computer Vision and Artificial Intelligence |
| title_short | Automatic Monitoring Cheese Ripeness Using Computer Vision and Artificial Intelligence |
| title_sort | automatic monitoring cheese ripeness using computer vision and artificial intelligence |
| topic | Cheese ripening image analysis image processing machine learning image classification deep learning |
| url | https://ieeexplore.ieee.org/document/9956763/ |
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