Research Progress of Machine Learning in Extending and Regulating the Shelf Life of Fruits and Vegetables

• Published in: Foods Vol. 13; no. 19; p. 3025
• Main Authors: Li, Dawei, Bai, Lin, Wang, Rong, Ying, Sun
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 24.09.2024; MDPI
• Subjects: Accuracy; Agricultural wastes; Algorithms; Artificial intelligence; Datasets; Deep learning; Flavors; Food chains; Food industry; food processing; Food quality; Food safety; food safety and quality; Fruit; Fruits; Green building (Construction); Green development; Humidity; Industrial development; Innovations; Learning algorithms; Machine learning; Nondestructive testing; Physicochemical properties; Prediction models; Predictive control; Quality control; Quality management; Review; Robust control; Seasonal variations; Shelf life; shelf life prediction; Storage conditions; Supply chains; Support vector machines; Sustainable development; Vegetables; vegetables and fruits; China; shelf life prediction; vegetables and fruits; machine learning; artificial intelligence; food processing; food safety and quality
• ISSN: 2304-8158; 2304-8158
• DOI: 10.3390/foods13193025

Fruits and vegetables are valued for their flavor and high nutritional content, but their perishability and seasonality present challenges for storage and marketing. To address these, it is essential to accurately monitor their quality and predict shelf life. Unlike traditional methods, machine learning efficiently handles large datasets, identifies complex patterns, and builds predictive models to estimate food shelf life. These models can be continuously refined with new data, improving accuracy and robustness over time. This article discusses key machine learning methods for predicting shelf life and quality control of fruits and vegetables, with a focus on storage conditions, physicochemical properties, and non-destructive testing. It emphasizes advances such as dataset expansion, model optimization, multi-model fusion, and integration of deep learning and non-destructive testing. These developments aim to reduce resource waste, provide theoretical basis and technical guidance for the formation of modern intelligent agricultural supply chains, promote sustainable green development of the food industry, and foster interdisciplinary integration in the field of artificial intelligence.