Utilizing RGB Imaging and Machine Learning for freshness level determination of green bell pepper (Capsicum annuum L.) throughout its shelf-life
This study investigates the sensory qualities, weight loss and texture changes in green bell peppers as indicator of freshness during storage. It assesses the feasibility of using machine learning methods to monitor freshness changes in the commercial variety ‘Kyohikari’ over a 16-d storage period a...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025
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| Online Access: | http://psasir.upm.edu.my/id/eprint/115681/ http://psasir.upm.edu.my/id/eprint/115681/1/115681.pdf |
| _version_ | 1848866839884464128 |
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| author | Fatchurrahman, Danial Hilaili, Maulidia Nurwahyuningsih Russo, Lucia Jahari, Mahirah Fathi-Najafabadi, Ayoub |
| author_facet | Fatchurrahman, Danial Hilaili, Maulidia Nurwahyuningsih Russo, Lucia Jahari, Mahirah Fathi-Najafabadi, Ayoub |
| author_sort | Fatchurrahman, Danial |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This study investigates the sensory qualities, weight loss and texture changes in green bell peppers as indicator of freshness during storage. It assesses the feasibility of using machine learning methods to monitor freshness changes in the commercial variety ‘Kyohikari’ over a 16-d storage period at + 5 °C and 95 % relative humidity (RH). Throughout the storage period, the commercial variety of green bell peppers were stored, and RGB images were captured using a DSLR camera. Sensory assessments and measurements of texture, weight loss, chlorophyll, and carotenoids were conducted at various inteval. Several machine learning approaches- including logistic regression, neural networks, random forests, k-nearest neighbors, and support vector machines, were employed to develop classification and prediction models for fruit freshness during storage intervals of 0, 4, 6, 8, 10, 12, 14, and 16 d. The results indicate that hue angle and chlorophyll content remained unchanged throughout the experiment. However, after 10 d of storage, a 3 % weight loss was observed, accompanied by the detection of off-odors and off-flavors, marking the limit of marketability. The models demonstrated exceptional accuracy in classifying and predicting the freshness of green bell peppers on the storage day, achieving 100 % accuracy with the neural network algorithm. |
| first_indexed | 2025-11-15T14:26:59Z |
| format | Article |
| id | upm-115681 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:26:59Z |
| publishDate | 2025 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1156812025-03-10T04:36:16Z http://psasir.upm.edu.my/id/eprint/115681/ Utilizing RGB Imaging and Machine Learning for freshness level determination of green bell pepper (Capsicum annuum L.) throughout its shelf-life Fatchurrahman, Danial Hilaili, Maulidia Nurwahyuningsih Russo, Lucia Jahari, Mahirah Fathi-Najafabadi, Ayoub This study investigates the sensory qualities, weight loss and texture changes in green bell peppers as indicator of freshness during storage. It assesses the feasibility of using machine learning methods to monitor freshness changes in the commercial variety ‘Kyohikari’ over a 16-d storage period at + 5 °C and 95 % relative humidity (RH). Throughout the storage period, the commercial variety of green bell peppers were stored, and RGB images were captured using a DSLR camera. Sensory assessments and measurements of texture, weight loss, chlorophyll, and carotenoids were conducted at various inteval. Several machine learning approaches- including logistic regression, neural networks, random forests, k-nearest neighbors, and support vector machines, were employed to develop classification and prediction models for fruit freshness during storage intervals of 0, 4, 6, 8, 10, 12, 14, and 16 d. The results indicate that hue angle and chlorophyll content remained unchanged throughout the experiment. However, after 10 d of storage, a 3 % weight loss was observed, accompanied by the detection of off-odors and off-flavors, marking the limit of marketability. The models demonstrated exceptional accuracy in classifying and predicting the freshness of green bell peppers on the storage day, achieving 100 % accuracy with the neural network algorithm. Elsevier 2025-04 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/115681/1/115681.pdf Fatchurrahman, Danial and Hilaili, Maulidia and Nurwahyuningsih and Russo, Lucia and Jahari, Mahirah and Fathi-Najafabadi, Ayoub (2025) Utilizing RGB Imaging and Machine Learning for freshness level determination of green bell pepper (Capsicum annuum L.) throughout its shelf-life. Postharvest Biology and Technology, 222. art. no. 113359. pp. 1-10. ISSN 0925-5214; eISSN: 0925-5214 https://www.sciencedirect.com/science/article/pii/S0925521424006045?via%3Dihub 10.1016/j.postharvbio.2024.113359 |
| spellingShingle | Fatchurrahman, Danial Hilaili, Maulidia Nurwahyuningsih Russo, Lucia Jahari, Mahirah Fathi-Najafabadi, Ayoub Utilizing RGB Imaging and Machine Learning for freshness level determination of green bell pepper (Capsicum annuum L.) throughout its shelf-life |
| title | Utilizing RGB Imaging and Machine Learning for freshness level determination of green bell pepper (Capsicum annuum L.) throughout its shelf-life |
| title_full | Utilizing RGB Imaging and Machine Learning for freshness level determination of green bell pepper (Capsicum annuum L.) throughout its shelf-life |
| title_fullStr | Utilizing RGB Imaging and Machine Learning for freshness level determination of green bell pepper (Capsicum annuum L.) throughout its shelf-life |
| title_full_unstemmed | Utilizing RGB Imaging and Machine Learning for freshness level determination of green bell pepper (Capsicum annuum L.) throughout its shelf-life |
| title_short | Utilizing RGB Imaging and Machine Learning for freshness level determination of green bell pepper (Capsicum annuum L.) throughout its shelf-life |
| title_sort | utilizing rgb imaging and machine learning for freshness level determination of green bell pepper (capsicum annuum l.) throughout its shelf-life |
| url | http://psasir.upm.edu.my/id/eprint/115681/ http://psasir.upm.edu.my/id/eprint/115681/ http://psasir.upm.edu.my/id/eprint/115681/ http://psasir.upm.edu.my/id/eprint/115681/1/115681.pdf |