A computer vision system and machine learning algorithms for prediction of physicochemical changes and classification of coated sweet cherry
The current research utilized visual characteristics obtained from RGB images and qualitative characteristics to investigate changes in surface defects, predict physical and chemical characteristics, and classify sweet cherries during storage. It was achieved with the help of ANN (Artificial Neural...
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| Language: | English |
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Elsevier
2024-10-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024155156 |
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| author | Yashar Shahedi Mohsen Zandi Mandana Bimakr |
| author_facet | Yashar Shahedi Mohsen Zandi Mandana Bimakr |
| author_sort | Yashar Shahedi |
| collection | DOAJ |
| description | The current research utilized visual characteristics obtained from RGB images and qualitative characteristics to investigate changes in surface defects, predict physical and chemical characteristics, and classify sweet cherries during storage. It was achieved with the help of ANN (Artificial Neural Network) and ANFIS (Adaptive Neuro-Fuzzy Inference System) models. The ANN used in this study was a Multilayer Perceptron (MLP) with SigmoidAxon and TanhAxon threshold functions, trained with the Momentum training function. Additionally, ANFIS with a Mamdani system and Triangle, Gauss, and Trapezoidal membership functions, was employed to predict sweet cherries' physical and chemical properties and their quality classification. Both models incorporate four algorithms. Additionally, the algorithms use color statistical features and color texture features combined with physical and chemical properties, including weight loss, firmness, titratable acidity, and total anthocyanin content. The image color and texture characteristics were used by ANN and ANFIS models to predict physical and chemical properties with high accuracy. ANN and ANFIS models accurately estimate sweet cherry quality grades in all four algorithms with over 90 % accuracy. According to the findings, the ANN and ANFIS models have demonstrated satisfactory performance in the qualitative classification and prediction of sweet cherries' physical and chemical properties. |
| format | Article |
| id | doaj-art-b2f61e1975f94c2396c0fcbf2f267f56 |
| institution | OA Journals |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-b2f61e1975f94c2396c0fcbf2f267f562025-08-20T02:14:03ZengElsevierHeliyon2405-84402024-10-011020e3948410.1016/j.heliyon.2024.e39484A computer vision system and machine learning algorithms for prediction of physicochemical changes and classification of coated sweet cherryYashar Shahedi0Mohsen Zandi1Mandana Bimakr2Department of Food Science and Engineering, Faculty of Agriculture, University of Zanjan, Zanjan, 45371-38791, IranCorresponding author.; Department of Food Science and Engineering, Faculty of Agriculture, University of Zanjan, Zanjan, 45371-38791, IranDepartment of Food Science and Engineering, Faculty of Agriculture, University of Zanjan, Zanjan, 45371-38791, IranThe current research utilized visual characteristics obtained from RGB images and qualitative characteristics to investigate changes in surface defects, predict physical and chemical characteristics, and classify sweet cherries during storage. It was achieved with the help of ANN (Artificial Neural Network) and ANFIS (Adaptive Neuro-Fuzzy Inference System) models. The ANN used in this study was a Multilayer Perceptron (MLP) with SigmoidAxon and TanhAxon threshold functions, trained with the Momentum training function. Additionally, ANFIS with a Mamdani system and Triangle, Gauss, and Trapezoidal membership functions, was employed to predict sweet cherries' physical and chemical properties and their quality classification. Both models incorporate four algorithms. Additionally, the algorithms use color statistical features and color texture features combined with physical and chemical properties, including weight loss, firmness, titratable acidity, and total anthocyanin content. The image color and texture characteristics were used by ANN and ANFIS models to predict physical and chemical properties with high accuracy. ANN and ANFIS models accurately estimate sweet cherry quality grades in all four algorithms with over 90 % accuracy. According to the findings, the ANN and ANFIS models have demonstrated satisfactory performance in the qualitative classification and prediction of sweet cherries' physical and chemical properties.http://www.sciencedirect.com/science/article/pii/S2405844024155156Adaptive neuro-fuzzy inference systemClassificationArtificial neural networkComputer vision systemSweet cherry |
| spellingShingle | Yashar Shahedi Mohsen Zandi Mandana Bimakr A computer vision system and machine learning algorithms for prediction of physicochemical changes and classification of coated sweet cherry Heliyon Adaptive neuro-fuzzy inference system Classification Artificial neural network Computer vision system Sweet cherry |
| title | A computer vision system and machine learning algorithms for prediction of physicochemical changes and classification of coated sweet cherry |
| title_full | A computer vision system and machine learning algorithms for prediction of physicochemical changes and classification of coated sweet cherry |
| title_fullStr | A computer vision system and machine learning algorithms for prediction of physicochemical changes and classification of coated sweet cherry |
| title_full_unstemmed | A computer vision system and machine learning algorithms for prediction of physicochemical changes and classification of coated sweet cherry |
| title_short | A computer vision system and machine learning algorithms for prediction of physicochemical changes and classification of coated sweet cherry |
| title_sort | computer vision system and machine learning algorithms for prediction of physicochemical changes and classification of coated sweet cherry |
| topic | Adaptive neuro-fuzzy inference system Classification Artificial neural network Computer vision system Sweet cherry |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024155156 |
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