Forecasting Organic Crops Based on Machine Learning Algorithms

Saikat Banerjee; Debasmita Palsani; Abhoy Chand Mondal

doi:10.55524/ijirem.2024.11.5.14

Abstract

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.

Keywords

Organic, Machine Learning, Accuracy, Crop, Classification

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Cites this article as

S. Banerjee, D. Palsani, A. C. Mondal, "Forecasting Organic Crops Based on Machine Learning Algorithms", International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-11, Issue-5, Page No-98-103, 2024. Available from: https://doi.org/10.55524/ijirem.2024.11.5.14