Real-time monitoring and freshness classification of fresh bananas: practical insights

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Abstract

Fresh foods are essential products in the global food industry, offering consumers vital nutrients and health benefits worldwide. Despite advancements in freshness classification using image-based data, the literature lacks exploration of quantitative data in this field. In this study, we collected a real-world sensor dataset for food freshness classification during runtime by monitoring and recording environmental and chemical variables that affect food quality, using bananas as a case study. The collected dataset was pre-processed and used to train and test six machine learning models: logistic regression, random forest, support vector machine, K-Nearest Neighbor, decision tree, and gradient boosting. These models were employed for the automatic classification of banana freshness into three health classes: fresh, ripening, and spoiled. The results revealed that the random forest model outperforms other models in predicting banana health class, achieving an average accuracy of 95%. Additionally, we critically analyzed the collected data and provided actionable insights for stakeholders and professionals in the food industry, enabling them to make informed decisions that maintain product quality and reduce food waste.

Keywords

food freshness / classification / machine learning / sensor data / food waste

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This article has been retracted.Please see the Retraction Notice for more detail:
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