ARTIFICIAL INTELLIGENCE IN SUSTAINABLE AGRICULTURE: OPTIMIZING WATER USE IN THE CONTEXT OF CLIMATE CHANGE

Dragan Ugrinov; Magdalena Nikolic; Željko Grujičić; Brankica  Pažun; Zlatko  Langović

doi:10.59267/ekoPolj2602515U

Authors

Dragan Ugrinov University“Union Nikola Tesla” Belgrade, Faculty of Management, Njegoševa 1a, Sremski Karlovci, Serbia https://orcid.org/0000-0002-5387-820X
Magdalena Nikolic Academy of Applied Studies – Polytechnic, Katarine Ambrozić 3, Belgrade, Serbia https://orcid.org/0000-0002-2021-153X
Željko Grujičić University “Union - Nikola Tesla”, School of Engineering Management, Vojvode Mišića Boulevard no. 43, 11000 Belgrade, Serbia https://orcid.org/0000-0002-5488-0176
Brankica Pažun University “Union - Nikola Tesla”, School of Engineering Management, Vojvode Mišića Boulevard no. 43, 11000 Belgrade, Serbia https://orcid.org/0000-0002-9452-5064
Zlatko Langović University of Kragujevac, Faculty of Hotel Management and Tourism in Vrnjačka Banja, Vojvodjanska bb, 36210 Vrnjacka Banja, Serbia https://orcid.org/0000-0002-0248-0453

DOI:

https://doi.org/10.59267/ekoPolj2602515U

Keywords:

Artificial intelligence, Organic farming, Water optimization, AHP method, climate change, Sustainable irrigation

Abstract

This paper examines the application of artificial intelligence (AI) in organic agriculture using the Analytic Hierarchy Process (AHP). A multi-criteria analysis evaluated four key criteria: AI use in optimization, factors affecting precision irrigation, benefits of AI application, and implementation challenges, alongside three alternatives: price, personnel, and terrain. Results show that “implementation problems and challenges” are the most significant criterion (39.52%), while “personnel” is the most important alternative (63.45%), emphasizing the crucial role of human resources in adopting technological solutions. The study also demonstrates high consistency (CR = 0.0274), confirming the reliability of the findings. The paper highlights the need for institutional support, educational programs, improved water resource management, and affordable AI solutions, particularly for small farmers, while proposing directions for future research and policies to advance sustainable agriculture.

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