TY - JOUR A2 - Zhao, Yifan AU - Han, Junming AU - Li, Tong AU - He, Yun AU - Gao, Quan PY - 2022 DA - 2022/12/31 TI - Using Machine Learning Approaches for Food Quality Detection SP - 6852022 VL - 2022 AB - 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. SN - 1024-123X UR - https://doi.org/10.1155/2022/6852022 DO - 10.1155/2022/6852022 JF - Mathematical Problems in Engineering PB - Hindawi KW - ER -