Food Quality Prediction using Machine Learning Techniques

Abstract

Food quality assessment is a critical aspect of food safety, public health, and supply chain management. Traditional food quality inspection methods rely heavily on manual examination and laboratory testing, which are time-consuming, costly, and sometimes subjective. With the advancement of Artificial Intelligence (AI) and Machine Learning (ML), automated food quality prediction systems have gained significant attention.

This research paper proposes a machine learning-based approach for predicting food quality using physical, chemical, and visual attributes of food products. Various classification algorithms such as Logistic Regression, Support Vector Machine (SVM), Random Forest, and Convolutional Neural Networks (CNN) are analyzed. The experimental results show that machine learning models can accurately predict food quality and help reduce food wastage while ensuring consumer safety.