A cognitive agriculture framework for crop temperature prediction with semantic communication✩

Hao Liu; Xinyao Pan; Wenhan Long; Yonghui Wu; Lu Liu; John Panneerselvam; Rongbo Zhu

doi:10.1016/j.dcan.2025.07.013

›› 2026, Vol. 12 ›› Issue (1) :38 -51. DOI: 10.1016/j.dcan.2025.07.013

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A cognitive agriculture framework for crop temperature prediction with semantic communication^✩

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^a College of Informatics, Huazhong Agricultural University, Wuhan 430070, China

^b Department of Computer Science, University of Exeter, Exeter, UK

^* E-mail address: rbzhu@mail.hzau.edu.cn (R. Zhu).

Hao Liu received the B.S. degree and the M.S. degree from South-Central Minzu Uni-versity, Wuhan, China, in 2019 and 2022, respectively. He is currently pursuing the Ph.D. degree with the College of Informatics, Huazhong Agricultural University, Wuhan, China. His research interests include deep learning, semantic communication, smart agriculture, and the Internet of Things.

Xinyao Pan received the B.S. degree from Henan Agricultural University, Zhengzhou, China, in 2021. He is currently pursuing the M.S. degree with the College of Informat-ics, Huazhong Agricultural University, Wuhan, China. His research interests include deep learning and model compression.

Wenhan Long received the B.S. degree from South-Central Minzu University, Wuhan, China, in 2022 and the M.S. degree from Huazhong Agricultural University, Wuhan, China, in 2025. He is currently pursuing the Ph.D. degree with the College of Computer Science and Electronic Engineering, Hunan University, Changsha, China. His research interests include deep learning and the Internet of Things.

Yonghui Wu received the B.S. degree from South-Central Minzu University, Wuhan, China, in 2022 and the M.S. degree from Huazhong Agricultural University, Wuhan, China, in 2025. He is currently pursuing the Ph.D. degree with the College of Informat-ics, Huazhong Agricultural University, Wuhan, China. His research interests include deep learning, smart agriculture and model compression.

Lu Liu received the M.S. degree from Brunel University, Uxbridge, U.K., in 2003 and the Ph.D. degree from the University of Surrey, Guildford, U.K., in 2008. Dr. Liu is a Professor of Department of Computer Science, University of Exeter, Exeter, U.K. He was the Head of School of Informatics and a Professor of informatics, University of Leicester, Leicester, U.K. His research interests include data analytics, service computing, artificial intelligence, and the Internet of Things. He is a Fellow of British Computer Society.

John Panneerselvam received the M.Sc. degree in advanced computer networks and the Ph.D. degree in computing from the University of Derby, Derby, U.K., in 2013 and 2018, respectively. He is currently a Lecturer of Department of Computer Science, Univer-sity of Exeter, Exeter, U.K. His research interests include cloud computing, fog computing, the Internet of Things, big data analytics, opportunistic networking, and P2P computing. He is an active member of the British Computer Society and a HEA fellow. He won the Best Paper Award from the IEEE International Conference on Data Science and Systems, Exeter, in 2018.

Rongbo Zhu received the Ph.D. degree in communication and information systems from Shanghai Jiao Tong University, Shanghai, China, in 2006. From 2011 to 2012, Dr. Zhu was a visiting scholar with the CNSR Lab at Virginia Tech, USA. He is currently a Professor with the College of Informatics, Huazhong Agricultural University, Wuhan, China. He has published over 60 papers in the areas of wireless networks and mobile computing.

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Abstract

Accurate prediction of environmental temperature is pivotal for promoting sustainable crop growth. At present, the most eﬀective temperature sensing and prediction system is the Agricultural Internet of Things (AIoT), which deploys a large number of sensors to collect meteorological data and transmits them to the cloud server for prediction. However, this procedure is computationally and communicationally expensive for resource-constrained AIoT. Recently, Semantic Communication (SC) has shown potential in eﬃcient data transmission, but existing methods overlook the repetitive semantic information whilst sensing data, bringing additional overheads. With the resource-constraint nature of AIoT in mind, we propose the Semantic Communication-enabled Cognitive Agriculture Framework (SC-CAF) for delivering accurate temperature predictions. The proposed SC-CAF incorporates an intelligent analysis layer that performs the temperature prediction and model training and distribution, while a semantic layer transmitting the semantic information extracted from raw data based on the download model, ultimately to reduce communication overheads in AIoT. Furthermore, we propose a novel model called the Light Temperature Semantic Communication (LTSC) by adopting skip-attention and semantic compressor to avoid unnecessary computation and repetitive information, thereby addressing the semantic redundancy issues in sensing data. We also develop a Semantic-based Model Compression (SCMC) algorithm to alleviate the computation and bandwidth burden, enabling AIoT to explore the extensive usage of SC. Experimental results demonstrate that the proposed SC-CAF achieves the lowest prediction error while reducing Floating Point Operations (FLOPs) by 95.88%, memory requirements by 78.30%, Graphics Processing Unit (GPU) power by 50.77%, and time latency by 84.44%, outperforming notable state-of-the-art methods.

Keywords

Agricultural Internet of Things / Cognitive agriculture / Semantic communication / Temperature prediction / Model compression

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Hao Liu, Xinyao Pan, Wenhan Long, Yonghui Wu, Lu Liu, John Panneerselvam, Rongbo Zhu. A cognitive agriculture framework for crop temperature prediction with semantic communication^✩. , 2026, 12(1): 38-51 DOI:10.1016/j.dcan.2025.07.013

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CRediT authorship contribution statement

Hao Liu: Writing-review & editing, Writing-original draft, Visual-ization, Validation, Software, Methodology, Investigation, Formal anal-ysis, Data curation, Conceptualization. Xinyao Pan: Writing-original draft, Visualization, Validation, Software, Methodology, Data curation, Conceptualization. Wenhan Long: Writing-review & editing, Visual-ization, Validation, Software. Yonghui Wu: Writing-review & editing, Visualization, Validation, Software, Methodology. Lu Liu: Writing-re-view & editing, Supervision. John Panneerselvam: Writing-review & editing. Rongbo Zhu: Writing-review & editing, Writing-origi-nal draft, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Con-ceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was supported by the Key Research and Development Project of Hubei Province (No. 2024BAB070), China.

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