Machine Learning-Assisted Food Quality Index Determination for Healthcare

In the modern world, either directly or indirectly, the food industry is essential for human nutrition. Growing food safety concerns have increased the demand for global food safety monitoring in recent times. Food quality assurance is therefore an important means of ensuring food safety. Equipment, test kits, or crafts are used in the conventional methods. These methods require more labor power and more time. They are difficult to implement and difficult to detect. Thus, this paper uses Convolutional Neural Network (CNN), a machine learning approach that provides an efficient and non-destructive method for food quality detection. This study provides comprehensive experimental data demonstrating the effectiveness of convolutional neural networks in determining the quality and freshness of the food. It also uses the experimental data to explore the overfitting of the machine learning model.