Rzepak (Brassica napus L.) jest jedną z najważniejszych roślin oleistych na świecie. Coraz większe znaczenie w jego produkcji zyskujeuczenie maszynowe, które umożliwia prognozowanie plonów, ocenę stanu roślin oraz wspomaganie decyzji agrotechnicznych. W pracy przedstawiono przegląd badań dotyczących wykorzystania różnych algorytmów – od klasycznych modeli, takich jak regresja liniowa, drzewa decyzyjne, las losowy czy maszyna wektorów nośnych, po bardziej zaawansowane, w tym sieci neuronowe. Omówiono zasto­sowania danych z teledetekcji, obrazowania zarejestrowane z pokładu bezzałogowych statków powietrznych i satelitów oraz czujników naziemnych do monitorowania chorób, stresów środowiskowych i potrzeb nawozowych. Podkreślono potencjał uczenia maszynowego w zwiększaniu efektywności i zrównoważeniu produkcji rzepaku, a także wyzwania związane z jakością danych, złożonością modeli i ich interpretacją.

Oilseed rape (Brassica napus L.) is one of the most important oil crops worldwide. Machine learning metchods are gaining increasing im­portance in oilseed rape production, enabling yield prediction, crop condition assessment, and support for agronomic decision-making. This paper presents a review of studies on the use of various algorithms – from classical models such as linear regression, decision trees, random forest, and support vector machine to more advanced ones, including neural networks. Applications of remote sensing data, unnamed aerial vehicles and satellite imagery, as well as ground-based sensors for monitoring diseases, environmental stresses, and nutrient requirements are discussed. The potential of machine learning to enhance efficiency and sustainability in rapeseed production is highlighted, along with challenges related to data quality, model complexity, and interpretability.

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