Ding Yang, Yancheng Wang, Keqiang Sun, Yue Zhang, Yanan Wang, Yuru Ye, Yifan Hong, Yanchao Li, Wei Zhou, Jinhan Zhang, Zhaode Yin, Xiaohu Zhang, Liujun Xiao, Bing Liu, Leilei Liu, Tao Cheng, Xia Yao, Chongya Jiang, Hengbiao Zheng, Xiaojun Liu, Qiang Cao, Yongcao Tian, Yan Zhu, Weixing Cao, Yihua Zhu, Liang Tang. SmartFarming: a GIS-based agent framework for farm management. 2026. agriXiv.202610.00002
SmartFarming: a GIS-based agent framework for farm management
Corresponding author: Yihua Zhu, zhyh@njau.edu.cn; Liang Tang, tangl@njau.edu.cn
DOI: 10.12343/Agrixiv.202610.00002 CSTR: 31252.36.agrixiv.202610.00002
Statement: This article is a preprint and has not been peer-reviewed. It reports new research that has yet to be evaluated and so should not be used to guide clinical practice.
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Abstract: Objective: Farm management requires continuous decisions constrained by field conditions, crops, weather, soil, agricultural inputs and business objectives, yet workflows built on predefined software modules still fail to transfer field boundaries, variable semantics, parameter units and intermediate outputs across platforms and models. This study develops SmartFarming, a GIS-based agent framework that translates natural-language farm management requests into constrained, executable and traceable multistep workflows.Methods: The framework is organized around four components - Persona, Agent Harness, Capabilities and World Interface. The Agent Harness coordinates task orchestration, capability routing, context management, hierarchical memory, execution monitoring and governance. Skill contracts constrain capability composition, while artifacts and their manifests record spatial semantics, versions and data lineage. A GIS module connects field inputs, computation, result visualization and user review within a spatial feedback loop.Results: Nine case studies instantiate the framework: satellite remote sensing monitoring, UAV remote sensing for rice growth, ground RGB image analysis for wheat-head and rice-panicle counting and disease identification, crop growth prediction, and model-remote-sensing coupling for yield prediction. The cases show that SmartFarming can organize, compose and check existing agronomic models, data services and spatial interactions in end-to-end workflows.Conclusions: A GIS-based agent framework with explicit spatial, temporal and risk constraints provides a practical basis for reusing heterogeneous agronomic models and data services in farm management, and offers a transferable structure for building domain agent systems in agriculture.
Key words: farm management; agent harness; skill contract; geographic information system; remote sensingSubmit time: 23 October 2026
Copyright: The copyright holder for this preprint is the author/funder, who has granted agriXiv a license to display the preprint in perpetuity. -
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