Using Machine Learning Approaches for Food Quality Detection

Junming Han, Tong Li, Yun He, Quan Gao and Yifan Zhao

Mathematical Problems in Engineering, 2022, vol. 2022, 1-9

Abstract: Food quality detection is an important method for ensuring food safety. Efficient quality detection methods can improve the efficiency of food circulation and reduce storage and labor costs. Traditional methods use instrumentation, testing reagents, or manual labor. These methods take a long time to detect, are time-consuming and labor-intensive, and require professionals to operate. Fruit, as a high-value food that provides essential nutrition for human beings, is susceptible to spoilage during packaging, transportation, and sales, so the freshness and safety assurance of fruit are a hot and difficult area of current research. Therefore, for the detection of fruit freshness, this paper proposes an efficient and nondestructive way to detect fruit freshness by using the machine learning algorithm convolutional neural network (CNN). This paper shows that convolutional neural networks have good performance in identifying the freshness of fruits through extensive experimental results and discusses the overfitting of machine learning based on the experimental results.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/mpe/2022/6852022.pdf (application/pdf)
http://downloads.hindawi.com/journals/mpe/2022/6852022.xml (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:6852022

DOI: 10.1155/2022/6852022

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().