Artificial intelligence-based multi-objective optimization in fruit and vegetable drying systems: A comprehensive review

Abstract

Fruits and vegetables are high in moisture content and rich in nutrients, but are perishable and have a short shelf life. Drying is a crucial technology to extend shelf life and reduce resource waste. However, traditional drying methods are limited by low efficiency, high energy consumption, and unstable product quality. Artificial intelligence (AI), with its self-learning, adaptive capabilities and ability of modeling complex non-linear relationships, offers innovative solutions and effective tools to tackle these issues. This paper provides a comprehensive review of AI applications in fruits and vegetables drying. First, we introduce the fundamental theories of machine learning, deep learning, fuzzy logic, expert system, computer vision system and their application advantages in fruits and vegetables drying. Subsequently, we elaborate case studies of AI in drying process modeling and predicting, real-time quality monitoring, and intelligent control and optimization, and discusses their applications in optimizing traditional drying and integrating with field-assisted drying techniques. Moreover, the limitations of AI in fruits and vegetables drying and their future development trends are analyzed to offer additional insights for researchers in the field.