Non-destructive Intelligent Testing of the Quality and Safety of Fresh Agricultural Products Based on Machine Learning: Principles, Challenges, and Applications

Abstract:

With the development of the modern food industry and the advancement of globalization, food quality and safety have emerged as major concerns for the public. In the context of expanding global trade and supply chains, fresh agricultural products, including fruits and vegetables, meat, and aquatic products, encounter considerable challenges related to perishability and the preservation. It is imperative to develop accurate and rapid quality and safety testing to improve monitoring and provide consumer protection. Traditional detection methods, such as gas chromatography, high-performance liquid chromatography, and polymerase chain reaction, are often limited by stringent detection requirements, long detection cycles, and potential sample damage, hindering their large-scale application in the food industry. With the rapid development of artificial intelligence and computer technology, machine learning models with strong predictive abilities and high accuracy have been widely used in quality and safety inspections of fresh agricultural products, effectively addressing the limitations related to data redundancy and the processing of large datasets. This article provides a systematic overview of machine learning technology and deep learning algorithms and analyzes their applications in quality and safety inspections of fresh agricultural products. It also introduces the principles of several emerging non-destructive testing technologies, and discusses current research progress and potential application prospects for machine learning and non-destructive testing technologies in the food industry.