Predicting maize yield with a multilayer perceptron (MLP) model using multivariate field data

A tétel rövid nézete
dc.contributor.authorFejér, Péter
dc.contributor.authorSzéles, Adrienn
dc.contributor.authorRagán, Péter
dc.contributor.authorJuhász, Csaba
dc.contributor.authorHorváth, Éva
dc.contributor.authorRátonyi, Tamás
dc.date.accessioned2025-07-28T11:10:56Z
dc.date.available2025-07-28T11:10:56Z
dc.date.issued2025-07-22
dc.description.abstractThis study presents the findings of a multi-year maize field trial conducted on experimental plots between 2017 and 2019, focusing on the application of machine learning techniques to enhance yield prediction accuracy. A multilayer perceptron (MLP) neural network was employed to model the effects of agronomic treatments, environmental variation, and compositional traits. Six distinct modeling scenarios were developed to explore different combinations of input variables, with the grain yield of maize serving as the sole output parameter. These scenarios range from treatment-only models to those incorporating detailed quality and compositional data. The primary objective was to evaluate how well MLP models can capture the complex, nonlinear relationships influencing yield under varying conditions. The findings provide valuable insight into the role of machine learning in supporting decision-making for sustainable crop production, especially under diverse technological and environmental settings. The approach demonstrated here offers a foundation for more adaptable, data-driven strategies in agronomic optimization.en
dc.formatapplication/pdf
dc.identifier.citationPrecision Crop Production, Vol. 1 (2025): Precision Crop Production ,
dc.identifier.issue01
dc.identifier.jatitlepcp
dc.identifier.jtitlePrecision Crop Production
dc.identifier.urihttps://hdl.handle.net/2437/396201
dc.identifier.volume1
dc.languageen
dc.relationhttps://ojs.lib.unideb.hu/pcp/article/view/15904
dc.rights.accessOpen Access
dc.rights.ownerPéter Fejér, Adrienn Széles, Péter Ragán, Csaba Juhász, Éva Horváth, Tamás Rátonyi
dc.subjectmachine learningen
dc.subjectmaizeen
dc.subjectANNen
dc.subjectMLPen
dc.subjectneural networken
dc.subjectyielden
dc.subjectfield trialen
dc.subjecttillageen
dc.subjectnutrient supplyen
dc.titlePredicting maize yield with a multilayer perceptron (MLP) model using multivariate field dataen
dc.typefolyóiratcikkhu
dc.typearticleen
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