Volume- 11
Issue- 5
Year- 2024
DOI: 10.55524/ijirem.2024.11.5.14 | DOI URL: https://doi.org/10.55524/ijirem.2024.11.5.14 Crossref
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)
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Saikat Banerjee , Debasmita Palsani, Abhoy Chand Mondal
Classification of organic food is crucial to ensuring authenticity and consumer trust and distinguishing it from conventionally grown produce. Accurate labelling and certification help prevent fraud and guarantee adherence to organic farming standards. Machine learning methods are utilized to categorize organic food exclusively based on nutritional data by analysing macro and micronutrient profiles. This approach improves classification precision and offers significant insights into the dietary advantages of organic food. Moreover, the results might enhance consumer knowledge and facilitate informed dietary decisions by emphasizing the improved nutritional quality of organic products relative to conventional alternatives. In this research, we have used five different machine learning algorithms to classy organic food. Experimental results show that decision trees perform better than other machine learning-based models.
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State Aided College Teacher, Department of Computer Applications, Vivekananda Mahavidyalaya, Haripal, Hooghly, West Bengal, India
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