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Agriculture
Volume 15
Issue 20
10.3390/agriculture15202138
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Dragana Stevanović
1,
Vesna Perić
2,
Svetlana Roljević Nikolić
1,
Violeta Mickovski Stefanović
1,
Violeta Oro
3,
Marijenka Tabaković
2,* and
Ljubiša Kolarić
4
1
“Tamiš” Research and Development Institute, 26000 Pančevo, Serbia
2
Maize Research Institute Zemun Polje, 11185 Belgrade, Serbia
3
Institute of Plant Protection and Environment, 11000 Belgrade, Serbia
4
Faculty of Agriculture, University of Belgrade, 11080 Belgrade, Serbia
*
Author to whom correspondence should be addressed.
Agriculture 2025, 15(20), 2138; https://doi.org/10.3390/agriculture15202138
Submission received: 15 September 2025 / Revised: 7 October 2025 / Accepted: 11 October 2025 / Published: 14 October 2025
(This article belongs to the Special Issue Agronomic Strategies to Improve Adaptability and Stability of Maize Production Systems Under Climate Change)
Review Reports Versions Notes

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, R2 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 yield forecasting patterns; growth stages; cultivation techniques

Share and Cite

MDPI and ACS Style

Stevanović, D.; Perić, V.; Roljević Nikolić, S.; Mickovski Stefanović, V.; Oro, V.; Tabaković, M.; Kolarić, L. Predictive Modelling of Maize Yield Under Different Crop Density Using a Machine Learning Approach. Agriculture 2025, 15, 2138. https://doi.org/10.3390/agriculture15202138

AMA Style

Stevanović D, Perić V, Roljević Nikolić S, Mickovski Stefanović V, Oro V, Tabaković M, Kolarić L. Predictive Modelling of Maize Yield Under Different Crop Density Using a Machine Learning Approach. Agriculture. 2025; 15(20):2138. https://doi.org/10.3390/agriculture15202138

Chicago/Turabian Style

Stevanović, Dragana, Vesna Perić, Svetlana Roljević Nikolić, Violeta Mickovski Stefanović, Violeta Oro, Marijenka Tabaković, and Ljubiša Kolarić. 2025. "Predictive Modelling of Maize Yield Under Different Crop Density Using a Machine Learning Approach" Agriculture 15, no. 20: 2138. https://doi.org/10.3390/agriculture15202138

APA Style

Stevanović, D., Perić, V., Roljević Nikolić, S., Mickovski Stefanović, V., Oro, V., Tabaković, M., & Kolarić, L. (2025). Predictive Modelling of Maize Yield Under Different Crop Density Using a Machine Learning Approach. Agriculture, 15(20), 2138. https://doi.org/10.3390/agriculture15202138

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