Development of digital extension platforms as a direction of digital transformation of agricultural production
| dc.contributor.author | Sharov, Sergii | |
| dc.contributor.author | Pryima, Serhii | |
| dc.contributor.author | Sharova, Tetiana | |
| dc.contributor.author | Zinovieva, Olha | |
| dc.contributor.author | Шаров, Сергій Володимирович | |
| dc.contributor.author | Прийма, Сергій Миколайович | |
| dc.contributor.author | Шарова, Тетяна Михайлівна | |
| dc.contributor.author | Зінов’єва, Ольга Геннадіївна | |
| dc.date.accessioned | 2026-09-25T06:37:45Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | The article examines modern approaches to digital transformation in agricultural extension, including digital extension platforms, semantic technologies, and artificial intelligence. Traditional agricultural extension faces several limitations, including its strong dependence on human resources, regional shortages of qualified specialists, and logistical barriers. These challenges indicate the need to move from conventional information support models toward intelligent decision support systems aligned with the concept of “Agriculture 5.0”. Modern information and communication technologies offer a scalable, cost-effective approach to disseminating practical agricultural knowledge. Recent research demonstrates that intelligent chatbots can help reduce the cost of extension services, optimize the use of technological resources, minimize transaction costs for agricultural enterprises, and support timely managerial decision-making in response to biotic and climatic threats. The introduction of such intelligent assistants transforms the role of an extension advisor, shifting it from a provider of reference information to an expert consultant who supports complex managerial decisions and helps address non-standard production challenges. Within the framework of the research project No. 0126U000765 at Dmytro Motornyi Tavria State Agrotechnological University, a conceptual approach to developing an intelligent extension environment is proposed. The approach integrates semantic wiki technologies, ontology modeling, and generative artificial intelligence. The proposed concept involves developing and applying a specialized ontology, the Advisory Learning Ecosystem (ALE), to formalize unstructured agronomic knowledge. Integrating the ALE ontology with the Semantic MediaWiki platform and large language models creates the foundation for a flexible digital environment that supports networking among researchers, extension advisors, and farmers. | |
| dc.identifier.citation | Development of digital extension platforms as a direction of digital transformation of agricultural production / S. Sharov, S. Pryima, T. Sharova, O. Zinovieva. Věda a perspektivy. 2026. № 7(62). Pp. 11–19. DOI: https://doi.org/10.52058/2695-1592-2026-7(62)-11-19 | |
| dc.identifier.doi | https://doi.org/10.52058/2695-1592-2026-7(62)-11-19 | |
| dc.identifier.uri | https://elar.tsatu.edu.ua/handle/123456789/21588 | |
| dc.language.iso | en | |
| dc.publisher | Věda a perspektivy | |
| dc.subject | agricultural extension | |
| dc.subject | digital transformation | |
| dc.subject | Agriculture 5.0 | |
| dc.subject | generative artificial intelligence | |
| dc.subject | ontology modeling | |
| dc.title | Development of digital extension platforms as a direction of digital transformation of agricultural production | |
| dc.type | Article |
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