Data-Driven Optimisation of Irrigation Dose Using Machine-Learning Ensembles for Sustainable European Agriculture

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Palková Z., Žitňák M., Valíček J., Harničárová M., Holý M., Levák D., ...Daha Fazla

Agris On-line Papers in Economics and Informatics, cilt.4, sa.December, ss.81-99, 2026 (ESCI, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 4 Sayı: December
  • Basım Tarihi: 2026
  • Doi Numarası: 10.7160/aol.2025.170408
  • Dergi Adı: Agris On-line Papers in Economics and Informatics
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, ABI/INFORM, Aerospace Database, Geobase, Academic Search Ultimate (EBSCO), East & Central Europe Database (ProQuest), Natural Science Collection (ProQuest), Technology Collection (ProQuest)
  • Sayfa Sayıları: ss.81-99
  • Anahtar Kelimeler: Artificial irrigation, data, digital agriculture, machine learning, smart farming, statistical modelling
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • İstanbul Medipol Üniversitesi Adresli: Evet

Özet

This study focuses on predicting irrigation doses using digital technologies and statistical modelling to enhance water resource management in agriculture. Conducted as part of the CODECS project in the semi-arid Nitra region of Slovakia, this study aimed to evaluate the effectiveness of various irrigation systems and to develop predictive models for optimal irrigation doses. The methodology integrates environmental sensor data, agronomic models, and machine learning techniques, utilizing IoT sensors alongside Valley and Irriga control software. A significant challenge was the incompatibility of heterogeneous data from different sources, leading to the creation of a unified method-ology for data collection, validation, and analysis. Analytical tools, such as ex-ploratory data analysis, correlation techniques, and regression models, were employed to identify key factors affecting irrigation efficiency, including precipitation, temperature, soil moisture, and energy consumption. The findings aim to inform sustainable irrigation strategies that reduce water usage, enhance crop productivity, and safeguard soil resources under changing climatic con-ditions.