A Comprehensive Review of AI Methods in Agri-Food Engineering: Applications, Challenges, and Future Directions
Kewei Wu, Zhigang Ji, Hanyue Wang, Xiaoyan Shao, Haohan Li, Wence Zhang, Wa Kong, Jing Xia, Xu Bao
Abstract
The deep integration of artificial intelligence (AI) is a core driver for digitalization and intelligence in agricultural and food engineering, boosting production efficiency, resource optimization, and product quality. This review systematically analyzes AI’s application scenarios, technical pathways, and challenges across the agricultural value chain. It aims to develop a structured taxonomy of AI-driven technical application mechanisms in agriculture, highlighting their roles in optimizing core agricultural processes. A systematic literature review was conducted using reputable databases, including Google Scholar, IEEE Xplore, ScienceDirect, Web of Science, SpringerLink, and Scopus, focusing on peer-reviewed articles from the last decade. Findings show that AI-enhanced techniques improve product quality and safety inspection efficiency. However, challenges like multi-source data synchronization barriers, high intelligent equipment costs, and model adaptability limitations in complex agricultural environments remain. This review contributes to the field by providing a unified framework for understanding AI applications in agri-food engineering, identifying key research gaps, and highlighting pathways for sustainable technology adoption that can benefit diverse agricultural stakeholders.