Glasnik Zaštite Bilja, Vol. 49. No. 5., 2026.
Professional paper
Application of Machine Learning in Agriculture
Ivan Brandić
; Sveučilište u Zagrebu Agronomski fakultet, Zagreb, Hrvatska
*
Karlo Špelić
; Sveučilište u Zagrebu Agronomski fakultet, Zagreb, Hrvatska
Mateja Grubor
; Sveučilište u Zagrebu Agronomski fakultet, Zagreb, Hrvatska
Ante Galić
; Sveučilište u Zagrebu Agronomski fakultet, Zagreb, Hrvatska
Ana Matin
; Sveučilište u Zagrebu Agronomski fakultet, Zagreb, Hrvatska
* Corresponding author.
Abstract
Machine learning is increasingly applied in agriculture as a tool for processing large volumes of data and supporting decision-making in agricultural production. The development of precision and digital agriculture has enabled the collection of various types of data through sensors, weather stations, satellite imagery, unmanned aerial vehicles, GPS systems, and other digital technologies. Such data can be used to monitor soil and crop conditions, predict yields, optimise irrigation and fertilisation, and manage natural resources more efficiently. This paper outlines the importance of data sources and types in modern agriculture, the basic methodology for developing machine learning models, and the key advantages, limitations, and challenges associated with their application. Although machine learning offers significant opportunities to enhance the efficiency and sustainability of agricultural production, its implementation requires careful data management, appropriate computing infrastructure, and expert understanding of the production system.
Keywords
machine learning; agriculture; big data; precision agriculture; digital agriculture; data management
Hrčak ID:
353306
URI
Publication date:
26.10.2026.
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