Image Processing Model to Estimate Nutritional Values in Raw and Cooked Vegetables
The availability of high-calorie foods with contentious nutritional content has led to a worldwide increase in chronic disease. Therefore, monitoring of eating habits and practising healthy eating habits is recommended. Clinical diet assessment methods and mobile calorie tracking apps can be used to...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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FRUCT
2023-11-01
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| Series: | Proceedings of the XXth Conference of Open Innovations Association FRUCT |
| Subjects: | |
| Online Access: | https://www.fruct.org/publications/volume-34/fruct34/files/Yen.pdf |
| _version_ | 1827381518698283008 |
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| author | Tan Jo Yen Sivakumar Vengusamy Fabio Caraffini Stefan Kuhn Simon Colreavy-Donnelly |
| author_facet | Tan Jo Yen Sivakumar Vengusamy Fabio Caraffini Stefan Kuhn Simon Colreavy-Donnelly |
| author_sort | Tan Jo Yen |
| collection | DOAJ |
| description | The availability of high-calorie foods with contentious nutritional content has led to a worldwide increase in chronic disease. Therefore, monitoring of eating habits and practising healthy eating habits is recommended. Clinical diet assessment methods and mobile calorie tracking apps can be used to record daily food consumption but are often not user-friendly. Convenient image-based assessment models are currently available to recognise and estimate the nutritional value of foods directly from food images, but they do not consider how nutritional value changes after cooking. Consequently, VegeNet, a multi-output InceptionV3-based convolutional neural network model has been developed, which estimates the nutritional values of cooked and uncooked vegetables. The explicit use of the cooking state is the main contribution of this work. This deep learning model successfully classifies the food images at 97% accuracy and estimates the nutritional values at 15.30% mean relative error, making it suitable as a visual-based added food assessment solution. This can help users save time and avoid under-reporting problems. |
| first_indexed | 2024-03-08T13:56:24Z |
| format | Article |
| id | doaj.art-b9f0cb96651145a3846102f70152e11a |
| institution | Directory Open Access Journal |
| issn | 2305-7254 2343-0737 |
| language | English |
| last_indexed | 2024-03-08T13:56:24Z |
| publishDate | 2023-11-01 |
| publisher | FRUCT |
| record_format | Article |
| series | Proceedings of the XXth Conference of Open Innovations Association FRUCT |
| spelling | doaj.art-b9f0cb96651145a3846102f70152e11a2024-01-15T12:32:23ZengFRUCTProceedings of the XXth Conference of Open Innovations Association FRUCT2305-72542343-07372023-11-0134119110.23919/FRUCT60429.2023.10328158Image Processing Model to Estimate Nutritional Values in Raw and Cooked VegetablesTan Jo Yen0Sivakumar Vengusamy1Fabio Caraffini2Stefan Kuhn3Simon Colreavy-Donnelly4NielseniqAsia Pacific University of Technology and InnovationSwansea UniversityTartu UniversityUniversity of LimerickThe availability of high-calorie foods with contentious nutritional content has led to a worldwide increase in chronic disease. Therefore, monitoring of eating habits and practising healthy eating habits is recommended. Clinical diet assessment methods and mobile calorie tracking apps can be used to record daily food consumption but are often not user-friendly. Convenient image-based assessment models are currently available to recognise and estimate the nutritional value of foods directly from food images, but they do not consider how nutritional value changes after cooking. Consequently, VegeNet, a multi-output InceptionV3-based convolutional neural network model has been developed, which estimates the nutritional values of cooked and uncooked vegetables. The explicit use of the cooking state is the main contribution of this work. This deep learning model successfully classifies the food images at 97% accuracy and estimates the nutritional values at 15.30% mean relative error, making it suitable as a visual-based added food assessment solution. This can help users save time and avoid under-reporting problems.https://www.fruct.org/publications/volume-34/fruct34/files/Yen.pdfnutrition; dietary assessmentconvolutional neural networkinceptionv3deep learningimage processing |
| spellingShingle | Tan Jo Yen Sivakumar Vengusamy Fabio Caraffini Stefan Kuhn Simon Colreavy-Donnelly Image Processing Model to Estimate Nutritional Values in Raw and Cooked Vegetables Proceedings of the XXth Conference of Open Innovations Association FRUCT nutrition; dietary assessment convolutional neural network inceptionv3 deep learning image processing |
| title | Image Processing Model to Estimate Nutritional Values in Raw and Cooked Vegetables |
| title_full | Image Processing Model to Estimate Nutritional Values in Raw and Cooked Vegetables |
| title_fullStr | Image Processing Model to Estimate Nutritional Values in Raw and Cooked Vegetables |
| title_full_unstemmed | Image Processing Model to Estimate Nutritional Values in Raw and Cooked Vegetables |
| title_short | Image Processing Model to Estimate Nutritional Values in Raw and Cooked Vegetables |
| title_sort | image processing model to estimate nutritional values in raw and cooked vegetables |
| topic | nutrition; dietary assessment convolutional neural network inceptionv3 deep learning image processing |
| url | https://www.fruct.org/publications/volume-34/fruct34/files/Yen.pdf |
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