Artificial intelligence in agricultural education: Applications, challenges, and future directions with a focus on image recognition

Abstract

Artificial intelligence (AI)‐based image recognition technology is reshaping the digital transformation of agricultural education by enhancing practical teaching, intelligent assessment, and personalized learning. This review examines the educational applications of AI image recognition technologies in agricultural training environments, including virtual simulation systems, intelligent pest and disease diagnosis, adaptive learning platforms, and smart field teaching. The study further discusses the pedagogical value of AI‐supported agricultural education from the perspectives of experiential learning, digital competence development, and instructional innovation. Major challenges, including teacher digital literacy, infrastructure limitations, data privacy, and educational equity in rural areas, are also analyzed. Finally, future directions for AI‐enabled agricultural education are proposed, emphasizing interdisciplinary curriculum reform, intelligent learning ecosystems, and sustainable educational development. This review provides a theoretical and practical reference for promoting the intelligent transformation of agricultural education.