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

Area of Science:

• Agricultural Engineering
• Food Science
• Artificial Intelligence

Background:

• Fruits and vegetables are nutrient-rich but highly perishable, necessitating effective preservation methods.
• Traditional drying techniques face challenges like low efficiency, high energy use, and inconsistent product quality.

Purpose of the Study:

• To comprehensively review the applications of artificial intelligence (AI) in the drying of fruits and vegetables.
• To highlight AI's potential in overcoming limitations of conventional drying methods.

Main Methods:

• Review of fundamental AI theories including machine learning, deep learning, fuzzy logic, expert systems, and computer vision.
• Analysis of case studies on AI for drying process modeling, prediction, quality monitoring, and intelligent control.

Main Results:

• AI demonstrates significant advantages in modeling complex, non-linear relationships within drying processes.
• AI applications enable real-time quality monitoring and intelligent optimization of drying parameters.
• AI integration with traditional and assisted drying techniques shows promise for enhanced performance.

Conclusions:

• AI provides effective tools to improve efficiency, reduce waste, and ensure stable product quality in fruit and vegetable drying.
• Further research into AI limitations and future trends is crucial for advancing intelligent food preservation techniques.