Predictive Modelling of Maize Yield Under Different Crop Density Using a Machine Learning Approach

Dragana Stevanović, Vesna Perić, Svetlana Roljević Nikolić, Violeta Mickovski Stefanović, Violeta Oro, Marijenka Tabaković () and Ljubiša Kolarić
Additional contact information
Dragana Stevanović: “Tamiš” Research and Development Institute, 26000 Pančevo, Serbia
Vesna Perić: Maize Research Institute Zemun Polje, 11185 Belgrade, Serbia
Svetlana Roljević Nikolić: “Tamiš” Research and Development Institute, 26000 Pančevo, Serbia
Violeta Mickovski Stefanović: “Tamiš” Research and Development Institute, 26000 Pančevo, Serbia
Violeta Oro: Institute of Plant Protection and Environment, 11000 Belgrade, Serbia
Marijenka Tabaković: Maize Research Institute Zemun Polje, 11185 Belgrade, Serbia
Ljubiša Kolarić: Faculty of Agriculture, University of Belgrade, 11080 Belgrade, Serbia

Agriculture, 2025, vol. 15, issue 20, 1-24

Abstract: In the face of increasing climate variability, understanding the dynamics of plant-to-plant interactions within crops is becoming increasingly important. This study aimed to examine plant responses to varying intensities of inter-plant competition, induced bz different planting densities, to enhance the accuracy of future yield prediction models. Six hybrids were grown at three planting densities (S1, S4, S7). Grain yield and yield components were estimated at four developmental points during grain filling (V1 to V4). These regression models and machine learning (ML) were applied to predict maize production under variable weather conditions. The factor year was the main source of variability, with less favourable conditions in the second year (G2) reducing yield by approximately 1–2%. Lower planting density (S1) improved individual plant development and yield components, while maximum density (S7) resulted in higher grain yield despite reduced individual performance. Hybrid H5 showed strong tolerance to high density, producing the highest yield under S7 conditions. Machine learning models accurately predicted key seed quality traits—moisture, oil, and protein—with performance metrics exceeding 80% accuracy. Specifically, R 2 values reached 0.82 for moisture content and 0.77 for oil concentration, indicating strong predictive capability. These findings support careful selection of hybrids and optimal planting density strategies in future cropping systems to increase yield and maintain seed quality in different environments.

Keywords: yield forecasting patterns; growth stages; cultivation techniques (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/20/2138/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/20/2138/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:20:p:2138-:d:1771234

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().