An Artificial Intelligence Approach Toward Food Spoilage Detection and Analysis
Aiming to increase the shelf life of food, researchers are moving toward new methodologies to maintain the quality of food as food grains are susceptible to spoilage due to precipitation, humidity, temperature, and a variety of other influences. As a result, efficient food spoilage tracking schemes...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-01-01
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| Series: | Frontiers in Public Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2021.816226/full |
| _version_ | 1819283418751434752 |
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| author | Ekta Sonwani Urvashi Bansal Roobaea Alroobaea Abdullah M. Baqasah Mustapha Hedabou |
| author_facet | Ekta Sonwani Urvashi Bansal Roobaea Alroobaea Abdullah M. Baqasah Mustapha Hedabou |
| author_sort | Ekta Sonwani |
| collection | DOAJ |
| description | Aiming to increase the shelf life of food, researchers are moving toward new methodologies to maintain the quality of food as food grains are susceptible to spoilage due to precipitation, humidity, temperature, and a variety of other influences. As a result, efficient food spoilage tracking schemes are required to sustain food quality levels. We have designed a prototype to track food quality and to manage storage systems at home. Initially, we have employed a Convolutional Neural Network (CNN) model to detect the type of fruit and veggies. Then the proposed system monitors the gas emission level, humidity level, and temperature of fruits and veggies by using sensors and actuators to check the food spoilage level. This would additionally control the environment and avoid food spoilage wherever possible. Additionally, the food spoilage level is informed to the customer by an alert message sent to their registered mobile numbers based on the freshness and condition of the food. The model employed proved to have an accuracy rate of 95%. Finally, the experiment is successful in increasing the shelf life of some categories of food by 2 days. |
| first_indexed | 2024-12-24T01:31:10Z |
| format | Article |
| id | doaj.art-5e8b52afaa0a45a99233a4304366571a |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-12-24T01:31:10Z |
| publishDate | 2022-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-5e8b52afaa0a45a99233a4304366571a2022-12-21T17:22:22ZengFrontiers Media S.A.Frontiers in Public Health2296-25652022-01-01910.3389/fpubh.2021.816226816226An Artificial Intelligence Approach Toward Food Spoilage Detection and AnalysisEkta Sonwani0Urvashi Bansal1Roobaea Alroobaea2Abdullah M. Baqasah3Mustapha Hedabou4Department of Computer Science and Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, IndiaDepartment of Computer Science and Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, IndiaDepartment Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi ArabiaDepartment of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi ArabiaSchool of Computer Science, Mohammed VI Polytechnic University, Ben Guerir, MoroccoAiming to increase the shelf life of food, researchers are moving toward new methodologies to maintain the quality of food as food grains are susceptible to spoilage due to precipitation, humidity, temperature, and a variety of other influences. As a result, efficient food spoilage tracking schemes are required to sustain food quality levels. We have designed a prototype to track food quality and to manage storage systems at home. Initially, we have employed a Convolutional Neural Network (CNN) model to detect the type of fruit and veggies. Then the proposed system monitors the gas emission level, humidity level, and temperature of fruits and veggies by using sensors and actuators to check the food spoilage level. This would additionally control the environment and avoid food spoilage wherever possible. Additionally, the food spoilage level is informed to the customer by an alert message sent to their registered mobile numbers based on the freshness and condition of the food. The model employed proved to have an accuracy rate of 95%. Finally, the experiment is successful in increasing the shelf life of some categories of food by 2 days.https://www.frontiersin.org/articles/10.3389/fpubh.2021.816226/fullmachine learning for healthsmart systemfood spoilage detectionfood spoilage preventionsensorsIoMT |
| spellingShingle | Ekta Sonwani Urvashi Bansal Roobaea Alroobaea Abdullah M. Baqasah Mustapha Hedabou An Artificial Intelligence Approach Toward Food Spoilage Detection and Analysis Frontiers in Public Health machine learning for health smart system food spoilage detection food spoilage prevention sensors IoMT |
| title | An Artificial Intelligence Approach Toward Food Spoilage Detection and Analysis |
| title_full | An Artificial Intelligence Approach Toward Food Spoilage Detection and Analysis |
| title_fullStr | An Artificial Intelligence Approach Toward Food Spoilage Detection and Analysis |
| title_full_unstemmed | An Artificial Intelligence Approach Toward Food Spoilage Detection and Analysis |
| title_short | An Artificial Intelligence Approach Toward Food Spoilage Detection and Analysis |
| title_sort | artificial intelligence approach toward food spoilage detection and analysis |
| topic | machine learning for health smart system food spoilage detection food spoilage prevention sensors IoMT |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2021.816226/full |
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