A Comparative Study on Predicting Food Quality using Machine Learning Techniques
| Autor: | M. Anly Antony, R. Satheesh Kumar |
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| Rok vydání: | 2021 |
| Předmět: |
Research areas
business.industry Computer science media_common.quotation_subject 010401 analytical chemistry 04 agricultural and veterinary sciences Machine learning computer.software_genre Food safety 040401 food science 01 natural sciences Field (computer science) 0104 chemical sciences Domain (software engineering) 0404 agricultural biotechnology Quality (business) Artificial intelligence business Food quality computer media_common |
| Zdroj: | 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS). |
| DOI: | 10.1109/icaccs51430.2021.9441743 |
| Popis: | Machine Learning has been established to be a state of the art technology with a high number of successful cases in various research areas. These techniques have been applied widely on food quality evaluation in recent years. This paper reviews the recent developments in the field of estimating food quality and food safety using machine learning techniques. The primary focus is on the supply of defect-free, premium quality products to the consumers which is considered to be major factor for the competitiveness of manufacturing units. This paper provides a brief description of machine learning techniques and then a detailed comparative study of different techniques to grade different kinds of foods. We investigated many articles to resolve the problems in food domain which includes detecting the quality of fruits, vegetables, seafood, meat and dairy products. The results of this survey shows that machine learning techniques outperforms the conventional methods used in the food domain. |
| Databáze: | OpenAIRE |
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