{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:57:04Z","timestamp":1760057824291,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:00:00Z","timestamp":1740182400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R &amp; D Program of China","award":["2023YFB3907600"],"award-info":[{"award-number":["2023YFB3907600"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The automation of extracting targeted decision-support information is a key task for achieving intelligent agricultural management. Essentially, this involves structurally representing agricultural operations based on knowledge, unified modeling and relational management of elements such as natural resources, human\u2013land relationships, and spatiotemporal data. However, the traditional farmland supervision systems based on relational and object-oriented databases struggle to effectively integrate, model, and apply operational knowledge such as project requirements, work experience, policies, and regulations. This limits their application efficiency and automation level. Therefore, this paper proposes a modeling method for Farmland Supervision Operations Scenario Model (FSOSM) based on structured operational knowledge. First, by analyzing the elements, structure, and functions of farmland supervision business scenario, the paper abstracts \u201cnatural resources\u2014human society\u2014spatiotemporal data\u201d into 8 categories of scenario elements and 22 types of multidimensional semantic relationships. Next, the operational knowledge is structured and integrated into various modeling steps, including scenario element extraction, association, expression, and application, thereby enhancing the model\u2019s intelligent service capability. Finally, the model is applied in practice through visualization and service applications using the \u201cFarmland Non-Grain Conversion Supervision Operation Scenario of Guangdong Province, China\u201d as a case study. The model\u2019s practicality and superiority are demonstrated by comparing the processing flows and effects of this model and traditional farmland management systems in terms of efficiency, automation level, knowledge service capability, and versatility.<\/jats:p>","DOI":"10.3390\/ijgi14030100","type":"journal-article","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T05:36:47Z","timestamp":1740375407000},"page":"100","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["FSOSM: An Operational Knowledge Empowered Scenario Model for the Intelligent Farmland Supervision"],"prefix":"10.3390","volume":"14","author":[{"given":"Jiacheng","family":"Xu","sequence":"first","affiliation":[{"name":"School of Geosciences & Surveying Engineering, China University of Mining and Technology, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4780-9723","authenticated-orcid":false,"given":"Xuesheng","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Geosciences & Surveying Engineering, China University of Mining and Technology, Beijing 100083, China"}]},{"given":"Bingliang","family":"Cui","sequence":"additional","affiliation":[{"name":"Guangzhou Alpha Software Information Technology Co., Ltd., Guangzhou 510000, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.dcan.2022.02.004","article-title":"Building an Interoperable Space for Smart Agriculture","volume":"9","author":"Roussaki","year":"2023","journal-title":"Digit. Commun. Netw."},{"key":"ref_2","first-page":"45","article-title":"Remote Sensing and GIS Application in Agriculture and Natural Resource Management","volume":"19","author":"Gebeyehu","year":"2019","journal-title":"Int. J. Environ. Sci. Nat. Resour."},{"key":"ref_3","first-page":"35","article-title":"Preliminary Study of Farmland Protection Supervision System of Fujian Province","volume":"2","author":"Gao","year":"2009","journal-title":"Nat. Resour. Informatiz."},{"key":"ref_4","first-page":"7","article-title":"Thoughts on the construction of intelligent sensing monitoring and supervision system of natural resources covering \u2018sky, air, land and sea\u2019\u2014A case study of Jiangsu province","volume":"6","author":"Yi","year":"2021","journal-title":"Nat. Resour. Informatiz."},{"key":"ref_5","first-page":"631","article-title":"Exploration and practice of normalized monitoring mode of natural resources in Guangdong Province","volume":"30","author":"Guo","year":"2023","journal-title":"J. Spatio-Temporal Inf."},{"key":"ref_6","unstructured":"Jia, C., Qi, N., Xia, C., and Wang, R. (2022, January 10). Lifecycle Management of Farmland Protection with a Focus on Balance. Proceedings of the 2022 China Urban Planning Informatization Annual Conference and Annual Conference of the Academic Committee on the Application of New Technologies in Urban Planning of the China Urban Planning Society, Hefei, China."},{"key":"ref_7","first-page":"86","article-title":"Research on the Implementation of the \u201cField Chief System\u201d for Promoting Farmland Protection through Digital Supervision System: Taking Zhejiang Province as an Example","volume":"21","author":"Zhao","year":"2024","journal-title":"Land Resour. Her."},{"key":"ref_8","first-page":"333","article-title":"Digitalization and Big Data in Smart Farming\u2014A Review","volume":"8","author":"Iaksch","year":"2021","journal-title":"J. Manag. Anal."},{"key":"ref_9","first-page":"2147","article-title":"Regional regulation and interregional coordination of cultivated land protection in China from the perspective of \u201cGreater Food\u201d approach","volume":"78","author":"Zhu","year":"2023","journal-title":"Acta Geogr. Sin."},{"key":"ref_10","first-page":"18","article-title":"International Experience of Sustainable Intensification and Its Implications for the Protection of Cultivated Land in China","volume":"34","author":"Peng","year":"2020","journal-title":"China Land Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"476","DOI":"10.36899\/japs.2024.2.0733","article-title":"Advantages of using expert systems to project dairy cattle farms: Case study of menemen","volume":"34","author":"Alkan","year":"2024","journal-title":"Turkey. J. Anim. Plant Sci."},{"key":"ref_12","first-page":"100404","article-title":"Employing sensor network based opportunistic spectrum utilization for agricultural monitoring","volume":"27","author":"Bayrakdar","year":"2020","journal-title":"Sustain. Comput. Inform. Sys."},{"key":"ref_13","first-page":"858","article-title":"A mixed land cover spatio-temporal data model based on object-oriented and snapshot","volume":"45","author":"Li","year":"2016","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_14","first-page":"1","article-title":"Empirical investigation into static and dynamic coupling metrics","volume":"39","author":"Geetika","year":"2014","journal-title":"Acm Sigsoft Softw. Eng. Notes"},{"key":"ref_15","first-page":"25","article-title":"Expert system for diagnosis of diseases in cat using the Naive Bayes method","volume":"8","author":"Perkasa","year":"2023","journal-title":"J. Teknol. Inf. Univ. Lambung Mangkurat JTIULM"},{"key":"ref_16","first-page":"60","article-title":"Design of entity object model system in natural resources field based on graph database","volume":"2","author":"Zhou","year":"2021","journal-title":"Land Resour. Informatiz."},{"key":"ref_17","first-page":"124","article-title":"Construction of spatio-temporal big data platform in natural resource management","volume":"1","author":"Yang","year":"2020","journal-title":"Bull. Surv. Mapp."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Cao, C., Chen, H., Zhao, L., Arshad, J., Asyhari, T., and Wang, Y. (2024). Knowledge Science, Engineering and Management: 17th International Conference, Springer Nature.","DOI":"10.1007\/978-981-97-5498-4"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.compag.2011.02.005","article-title":"Functional requirements for a future farm management information system","volume":"76","author":"Pesonen","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","first-page":"1041","article-title":"Building natural resources surveying and monitoring technological system: Direction and research agenda","volume":"77","author":"Chen","year":"2022","journal-title":"Acta Geogr. Sin."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1080\/00045608.2012.689234","article-title":"Virtual Geographic Environment: A Workspace for Computer-Aided Geographic Experiments","volume":"103","author":"Lin","year":"2012","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2324959","DOI":"10.1080\/17538947.2024.2324959","article-title":"Virtual geo-cyber environments: Metaphorical visualization of virtual cyberspace with geographical knowledge","volume":"17","author":"Jiang","year":"2024","journal-title":"Int. J. Digit. Earth"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/17538947.2024.2356126","article-title":"Virtual geographical scene twin modeling: A combined data-driven and knowledge-driven method with bridge construction as a case study","volume":"17","author":"Zhu","year":"2024","journal-title":"Int. J. Digit. Earth"},{"key":"ref_24","first-page":"1","article-title":"Is the future of cartography the scenario science?","volume":"20","author":"Lu","year":"2018","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_25","first-page":"356","article-title":"Geographic Scenario: A Possible Foundation for Further Development of Virtual Geographic Environments","volume":"11","author":"Lv","year":"2017","journal-title":"Int. J. Digit. Earth"},{"key":"ref_26","first-page":"2230","article-title":"Organization and management of geographic scenes based on object space","volume":"79","author":"Jing","year":"2024","journal-title":"Acta Geogr. Sin."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1080\/15230406.2023.2293880","article-title":"Advancing indoor risk mapping for virus transmission of infectious diseases through geographic scenario simulation","volume":"51","author":"Shen","year":"2024","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Huang, Y., Yuan, M., Sheng, Y., Min, X., and Cao, Y. (2019). Using Geographic Ontologies and Geo-Characterization to Represent Geographic Scenario. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8120566"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1080\/17538947.2021.2025275","article-title":"Processes and Events in the Centre: A Dynamic Data Model for Representing Spatial Change","volume":"15","author":"He","year":"2022","journal-title":"Int. J. Digit. Earth."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"107497","DOI":"10.1016\/j.resconrec.2024.107497","article-title":"Application of Question Answering Systems for Intelligent Agriculture Production and Sustainable Management: A Review","volume":"204","author":"Yang","year":"2024","journal-title":"Resour. Conserv. Recycl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"012053","DOI":"10.1088\/1757-899X\/1032\/1\/012053","article-title":"A Review on the Methods for Big Data Analysis in Agriculture","volume":"1032","author":"Evstatiev","year":"2021","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/s10618-022-00891-8","article-title":"Improving Embedded Knowledge Graph Multi-Hop Question Answering by Introducing Relational Chain Reasoning. Data Min","volume":"37","author":"Jin","year":"2023","journal-title":"Knowl. Discov."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, S., Xu, Y., Fang, Y., Liu, Y., Sun, S., Xu, R., Zhu, C., and Zeng, M. (2022). Training Data Is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data 2022. arXiv.","DOI":"10.18653\/v1\/2022.acl-long.226"},{"key":"ref_34","unstructured":"Cao, C., Chen, H., Zhao, L., Arshad, J., Asyhari, T., and Wang, Y. (2024). Enhancing Question Embedding with Relation Chain for Multi-Hop KGQA. Proceedings of the Knowledge Science, Engineering and Management, Springer Nature."},{"key":"ref_35","unstructured":"(2024, November 27). GPT-4 Technical Report. Available online: https:\/\/arxiv.org\/abs\/2303.08774v3."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Short, N.M., Woodward-Greene, M.J., Buser, M.D., and Roberts, D.P. (2023). Scalable Knowledge Management to Meet Global 21st Century Challenges in Agriculture. Land, 12.","DOI":"10.3390\/land12030588"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, X., Li, Y., Wang, H., and Lv, M. (2023). MKBQA: Question Answering over Knowledge Graph Based on Semantic Analysis and Priority Marking Method. Appl. Sci., 13.","DOI":"10.3390\/app13106104"},{"key":"ref_38","unstructured":"Gu, Y., Pahuja, V., Cheng, G., and Su, Y. (2022). Knowledge Base Question Answering: A Semantic Parsing Perspective 2022. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lan, Y., He, G., Jiang, J., Jiang, J., Zhao, W.X., and Wen, J.-R. (2021). A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions 2021. arXiv.","DOI":"10.24963\/ijcai.2021\/611"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Chen, Y., and Xing, X. (2022, January 27\u201330). Constructing Dynamic Knowledge Graph Based on Ontology Modeling and Neo4j Graph Database. Proceedings of the 2022 5th International Conference on Artificial Intelligence and Big Data (ICAIBD), Chengdu, China.","DOI":"10.1109\/ICAIBD55127.2022.9820199"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"baad031","DOI":"10.1093\/database\/baad031","article-title":"TRSRD: A Database for Research on Risky Substances in Tea Using Natural Language Processing and Knowledge Graph-Based Techniques","volume":"2023","author":"Wang","year":"2023","journal-title":"Database"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Garc\u00eda, M.\u00c1., Garc\u00eda-S\u00e1nchez, F., and Valencia-Garc\u00eda, R. (2021). Knowledge-Based System for Crop Pests and Diseases Recognition. Electronics, 10.","DOI":"10.3390\/electronics10080905"},{"key":"ref_43","first-page":"74","article-title":"Research on Knowledge Graph Model About Rainstorm Disaster Based on Simple Event Model","volume":"36","author":"Wang","year":"2021","journal-title":"J. Catastrophology"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"\u00c9tienne, M. (2014). Companion Modelling: A Method of Adaptive and Participatory Research. Companion Modelling: A Participatory Approach to Support Sustainable Development, Springer.","DOI":"10.1007\/978-94-017-8557-0"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1111\/geoj.12248","article-title":"Role-Playing Games in Natural Resource Management and Research: Lessons Learned from Theory and Practice","volume":"184","author":"Wesselow","year":"2018","journal-title":"Geogr. J."},{"key":"ref_46","unstructured":"(2024, November 27). Guidelines for the Classification of Land and Sea Use in Territorial Spatial Surveys, Planning, and Use Regulation (Trial), Available online: https:\/\/m.mnr.gov.cn\/gk\/tzgg\/202011\/t20201120_2589186.html."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2340089","DOI":"10.1080\/17538947.2024.2340089","article-title":"Efficient distributed association management method of data, model, and knowledge for digital twin railway","volume":"17","author":"Guo","year":"2024","journal-title":"Int. J. Digit. Earth"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4934","DOI":"10.1080\/17538947.2023.2290569","article-title":"AugGKG: A grid-augmented geographic knowledge graph representation and spatio-temporal query model","volume":"16","author":"Han","year":"2023","journal-title":"Int. J. Digit. Earth"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1080\/13658816.2023.2298299","article-title":"A knowledge-guided visualization framework of disaster scenes for helping the public cognize risk information","volume":"38","author":"Zhu","year":"2024","journal-title":"Int. J. Geogr. Inf. Sci."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/14\/3\/100\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:40:30Z","timestamp":1760028030000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/14\/3\/100"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,22]]},"references-count":49,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["ijgi14030100"],"URL":"https:\/\/doi.org\/10.3390\/ijgi14030100","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2025,2,22]]}}}