Smart farming is undergoing a profound transformation driven by advancements in artificial intelligence and data-centric technologies. As agriculture increasingly integrates sensors, imaging systems, environmental data, and biological knowledge, AI has become essential for enabling precise, efficient, and sustainable farm management. Recent breakthroughs in computer vision, natural language processing (NLP), multimodal large language models (LLMs), and knowledge graph–based reasoning offer unprecedented capabilities for understanding complex agro-ecosystems. These technologies enable automated crop and livestock monitoring, early disease detection, intelligent decision support, and large-scale knowledge mining from agricultural science and farm records.

Despite these opportunities, real-world deployment of AI in agriculture remains challenging due to heterogeneous data sources, dynamic environmental conditions, limited labelled datasets, and the need for interpretable and actionable insights. This Special Issue aims to bridge these gaps by highlighting innovative research that integrates vision, language, multimodal reasoning, and knowledge-driven analytics to solve practical challenges in modern agriculture. We seek contributions that demonstrate the transformative potential of AI to accelerate crop breeding, optimise farm operations, enhance biosecurity, and support climate-resilient agriculture.

Topics of interest include, but are not limited to:

We invite original research papers in, but not limited to, the following areas: Agricultural data governance and management; Human–AI Collaboration Agricultural Systems; advanced computer vision for crop, soil, and livestock intelligence; multimodal LLMs for agricultural decision-making; knowledge graph construction and reasoning for biological and ecological understanding; NLP for mining agronomic literature and farm reports; multimodal sensing fusion; autonomous farming systems; AI-powered crop breeding and phenotype analysis; environmental monitoring and yield prediction; and responsible, explainable, and trustworthy AI for agricultural applications. Collectively, these innovations will advance the next generation of intelligent farming systems that are more productive, sustainable, and resilient.

Guest Editors

Dr. Xiaohan Yu, Macquarie University, Australia

Dr. Yongsheng Gao, Griffith University, Australia

Dr. Xianxun Zhu, Nanyang Technological University, Singapore

Dr. Zaiwen Feng, Huazhong Agricultural University, China

Deadline

The deadline for manuscript submissions is 31 December 2026, but we can accommodate extensions on a case-by-case basis. Manuscripts submitted before the deadline will be subject to an APC of $2750 USD. All accepted papers will be published online.

Submission Instructions

Please submit the full manuscript to The Knowledge Engineering Review via our Online Submission System. All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors for submission of manuscripts is available on the For Authors page.

Additionally, please choose the topic of this Special Issue when submitting and specify it in your cover letter. For further inquiries, please contact Guest Editors:

Xiaohan Yu (xiaohan.yu@mq.edu.au)

Yongsheng Gao (yongsheng.gao@griffith.edu.au)

Xianxun Zhu (xianxun@sentic.net)

Zaiwen Feng (Zaiwen.Feng@mail.hzau.edu.cn)