AI Based Food Health Rating System

Abstract

Deep learning has emerged as an advanced technology for analyzing large volumes of data and has demonstrated significant success in applications such as image processing, speech recognition, and object detection. In recent years, this technology has also been introduced into the fields of food science and agricultural business. This paper provides a brief overview of deep learning and explains the architecture of several widely used deep neural network models along with the process involved in training them. Various research studies that employ deep learning as a data analysis technique are reviewed to address challenges in the food sector, particularly in the quality assessment of fruits and vegetables. The review examines key aspects of these studies, including the specific problems addressed, datasets used, preprocessing methods applied, network architectures and frameworks implemented, performance results achieved, and comparisons with other existing solutions. The primary objective of this work is to enhance understanding of these methods and demonstrate their practical applications in real-world scenarios. Food quality and food safety have long been important issues, yet they are often neglected in many systems. In recent years, these concerns have become even more critical due to the need for efficient on-demand supply chain management and improved profitability in agri-business sectors. However, with the development of modern technologies and intelligent systems, these challenges can now be addressed more effectively through the application of artificial intelligence and advanced analytical methods.