Применение онтологического подхода к проектированию геопространственной базы опытных данных для информационного обеспечения исследований в точном земледелии

Olga A. Mitrofanova; Evgenii P. Mitrofanov; Nataliya A. Bure

doi:10.21638/11701/spbu10.2022.206

Authors

Olga A. Mitrofanova Agrophysical Research Institute, 14, Grazhdanskiy pr., St Petersburg, 195220, Russian Federation
Evgenii P. Mitrofanov Agrophysical Research Institute, 14, Grazhdanskiy pr., St Petersburg, 195220, Russian Federation
Nataliya A. Bure St. Petersburg State University, 199034, St. Petersburg, Russian Federation

DOI:

https://doi.org/10.21638/11701/spbu10.2022.206

Abstract

Thanks to the development of information technologies and computing resources, it became possible to obtain and process big data, including geospatial data. Most research in the field of precision farming is interdisciplinary in nature, with experimental field data used by disparate scientific groups. In this connection, it became necessary to develop a unified web-based system for storing, organizing, and exchanging experimental information between researchers. The first step in achieving this goal was to create a geospatial database. Since the system being developed in the future may require extensions, modifications, adjustments, integration into other projects, it seems appropriate to use the ontology to form the database structure. The most popular tools were used as the main tools: the ontology language OWL (Ontology Web Language), the Protege 5.5 development environment. The main initial information obtained in the course of experimental studies carried out at the biopolygon: weather data, agrochemical indicators (sampling of soil and plants with georeferencing), agrophysical parameters (humidity, electrical conductivity), remote sensing data. Based on the results of the analysis of the current state of research in the field of storage and systematization of experimental information in crop production, as well as a survey of ARI employees, a prototype of the database structure was formed based on the ontological approach. Nine parent classes were defined as the foundation: Field, Crop rotation — experience, Agrotechnology, Yield, Meteo, Ground samples, Orthophoto, Calendar, and Dictionary — units of measurement.

Keywords:

ontology, precision agriculture, field experiments, biopolygon, OWL, Protege

Downloads

Download data is not yet available.

References

Литература

Якушев В. П., Буре В. М., Митрофанова О. А., Митрофанов Е. П. К вопросу автоматизации построения вариограмм в задачах точного земледелия // Вестник Санкт-Петербургского университета. Прикладная математика. Информатика. Процессы управления. 2020. Т. 16. Вып. 2. С. 177–185.

Буре В. М., Митрофанов Е. П., Митрофанова О. А., Петрушин А. Ф. Выделение однородных зон сельскохозяйственного поля для закладки опытов с помощью беспилотного летательного аппарата // Вестник Санкт-Петербургского университета. Прикладная математика. Информатика. Процессы управления. 2018. Т. 14. Вып. 2. С. 145–150. https://doi.org/10.21638/11701/spbu10.2018.206

Митрофанов Е. П., Митрофанова О. А., Буре В. М. Перспективы создания единой системы хранения и обработки данных дистанционного зондирования для мониторинга состояния посевов // Информационно-ресурсная цифровая платформа развития сельского хозяйства: сб. материалов конференции в рамках выставки «Агрорусь», Санкт-Петербург, 2–5 сентября 2020 г. СПб., 2020. С. 32–35.

Su X., Li J., Cui Y., Meng X., Wang Y. Review on the work of agriculture ontology research group // Journal of Integrative Agriculture. 2012. Vol. 11. N 5. P. 720–730.

Wei Y., Wang R., Hu Y., Wang X. From web resources to agricultural ontology: a method for semi-automatic construction // Journal of Integrative Agriculture. 2012. Vol. 11. N 5. P. 775–783.

Anandhi V., Venkitapirabu J., Natarajan S. K., Sumathi C. S. Ontology for crop management — A core vocabulary of agricultural activity // International Journal of Current Microbiology and Applied Sciences. 2020. Vol. 9. N 9. P. 3364–3368.

Abbasi R., Martinez P., Ahmad R. An ontology model to represent aquaponics 4.0 system’s knowledge // Information Processing in Agriculture. 2021.

Bonacin R., Nabuco O. F., Pierozzi Jr. I. Ontology models of the impacts of agriculture and climate changes on water resources: Scenarios on interoperability and information recovery // Future Generation Computer Systems. 2016. Vol. 54. P. 423–434.

Pakdeetrakulwong U., Hengpraprohm K. An ontology-based knowledge management for organic and good agricultural practice agriculture: A case study of Nakhon Pathom Province, Thailand // Thai Interdisciplinary Research. 2018. Vol. 13. N 4. P. 26–34.

Qin X., Zhang H., Zheng H. Research on intelligent retrieval system for agricultural information resources based on ontology // IOP Conference. Journal of Physics: Conference Series. 2019. Vol. 1168. N 022041.

Drury B., Fernandes R., Moura M. F., Lopes A. A. A survey of semantic web technology for agriculture // Information Processing in Agriculture. 2019. Vol. 6. P. 487–501.

Zheng Y., He Q., Qian P., Li Z. Construction of the ontology-based agricultural knowledge management system // Journal of Integrative Agriculture. 2012. Vol. 11. N 5. P. 700–709.

Li D., Kang L., Cheng X., Li D., Ji L., Wang K., Chen Y. An ontology-based knowledge representation and implement method for crop cultivation standard // Mathematical and computer modelling. 2013. Vol. 58. P. 466–473.

Mouromtsev D. Semantic reference model for individualization of information processes in IoT heterogeneous environment // Electronics. 2021. N 2523.

Wang X., Chen N., Chen Z., Yang X., Li J. Earth observation metadata ontology model for spatiotemporal-spectral semantic-enhanced satellite observation discovery: a case study of soil moisture monitoring // Journal GIScience and Remote Sensing. 2016. Vol. 53. Iss. 1. P. 22–44.

Gruber T. R. Toward principles for the design of ontologies used for knowledge sharing // International Journal of Human-Computer Studies. 1995. Vol. 43. P. 907–928.

Ngo Q. H., Kechadi T., Le-Khac N.-A. OAK: Ontology-based knowledge map model for digital agriculture // arXiv. 2020. N 2011.11442.

Gupta S., Sabitha A. S. Designing ontology for massive open online courses using Protege // 8^th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), June 4–5, 2020. India, 2020. P. 403–406.

Jambhulkar S. V., Karale S. J. Semantic web application generation using Protege tool // Online International Conference on Green Engineering and Technologies (IC-GET). 2016. N 39794780.

Официальный сайт PostgreSQL. URL: https://www.postgresql.org/ (дата обращения: 13.03. 2022).

References

Iakushev V. P., Bure V. M., Mitrofanova O. A., Mitrofanov E. P. K voprosu avtomatizatsii postroeniia variogramm v zadachakh tochnogo zemledeliia [On the issue of semivariograms constructing automation for precision agriculture problems]. Vestnik of Saint Peterburg University. Applied Mathematics. Computer Science. Control Processes, 2020, vol. 16, iss. 2, pp. 177–185. https://doi.org/10.21638/11701/spbu10.2020.209 (In Russian)

Bure V. M., Mitrofanov E. P., Mitrofanova O. A., Petrushin A. F. Vydelenie odnorodnykh zon sel'skokhoziaistvennogo polia dlia zakladki opytov s pomoshch'iu bespilotnogo letatel'nogo apparata [Selection of homogeneous zones of agricultural field for laying of experiments using unmanned aerial vehicle]. Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes, 2018, vol. 14, iss. 2, pp. 145–150.

Mitrofanov E. P., Mitrofanova O. A., Bure V. M. Perspektivy sozdaniia edinoi sistemy khraneniia i obrabotki dannykh distantsionnogo zondirovaniia dlia monitoringa sostoianiia posevov [Prospects of the unified system creation for storing and processing remote sensing data for monitoring the state of crops]. Information and resource digital platform for the development of agriculture. Collection of conference materials within the framework of the exhibition “Agrorus’ ’’. Saint Petersburg, September 2–5, 2020. St Petersburg, 2020, pp. 32–35. (In Russian)

Wei Y., Wang R., Hu Y., Wang X. From web resources to agricultural ontology: a method for semi-automatic construction. Journal of Integrative Agriculture, 2012, vol. 11, no. 5, pp. 775–783.

Anandhi V., Venkitapirabu J., Natarajan S. K., Sumathi C. S. Ontology for crop management — A core vocabulary of agricultural activity. International Journal of Current Microbiology and Applied Sciences, 2020, vol. 9, no. 9, pp. 3364–3368.

Abbasi R., Martinez P., Ahmad R. An ontology model to represent aquaponics 4.0 system’s knowledge. Information Processing in Agriculture, 2021.

Bonacin R., Nabuco O. F., Pierozzi Jr. I. Ontology models of the impacts of agriculture and climate changes on water resources: Scenarios on interoperability and information recovery. Future Generation Computer Systems, 2016, vol. 54, pp. 423–434.

Pakdeetrakulwong U., Hengpraprohm K. An ontology-based knowledge management for organic and good agricultural practice agriculture: A case study of Nakhon Pathom Province, Thailand. Thai Interdisciplinary Research, 2018, vol. 13, no. 4, pp. 26–34.

Qin X., Zhang H., Zheng H. Research on intelligent retrieval system for agricultural information resources based on ontology. IOP Conference. Journal of Physics: Conference Series, 2019, vol. 1168, no. 022041.

Zheng Y., He Q., Qian P., Li Z. Construction of the ontology-based agricultural knowledge management system. Journal of Integrative Agriculture, 2012, vol. 11, no. 5, pp. 700–709.

Li D., Kang L., Cheng X., Li D., Ji L., Wang K., Chen Y. An ontology-based knowledge representation and implement method for crop cultivation standard. Mathematical and computer modelling, 2013, vol. 58, pp. 466–473.

Mouromtsev D. Semantic reference model for individualization of information processes in IoT heterogeneous environment. Electronics, 2021, no. 2523.

Wang X., Chen N., Chen Z., Yang X., Li J. Earth observation metadata ontology model for spatiotemporal-spectral semantic-enhanced satellite observation discovery: a case study of soil moisture monitoring. Journal GIScience and Remote Sensing, 2016, vol. 53, iss. 1, pp. 22–44.

Gruber T. R. Toward principles for the design of ontologies used for knowledge sharing. International Journal of Human-Computer Studies, 1995, vol. 43, pp. 907–928.

Ngo Q. H., Kechadi T., Le-Khac N.-A. OAK: Ontology-based knowledge map model for digital agriculture. arXiv, 2020, no. 2011.11442.

Gupta S., Sabitha A. S. Designing ontology for massive open online courses using Protege. 8^th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), June 4–5, 2020. India, 2020, pp. 403–406.

Jambhulkar S. V., Karale S. J. Semantic web application generation using Protege tool. Online International Conference on Green Engineering and Technologies (IC-GET), 2016, no. 39794780.

Official website of PostgreSQL. Available at: urlhttps://www.postgresql.org/ (accessed: March 13, 2022).