Adaptive Capacity of Machine Learning Approaches for Leaf Area Estimation in Faba Bean Under Diverse Agro-environmental Conditions

Murat Kaya; Osman Varışlı; Abdurrahman Yılmaz; Ümit Acay; Sibel Ipekeşen; Leonardo Guevara; Behiye Bicer; Cafer Budak; Ravi Valluru

doi:10.15832/ankutbd.1782340

Adaptive Capacity of Machine Learning Approaches for Leaf Area Estimation in Faba Bean Under Diverse Agro-environmental Conditions

Abstract

In controlled, shaded agricultural environments, conventional remote sensing technologies are often ineffective due to their reliance on direct sunlight and sensitivity to variations in reflectance and absorption. These limitations reduce the effectiveness and precision of image-based leaf area estimation methods, especially in settings such as polytunnel farming, greenhouses, and areas covered by shade netting. Machine learning methods, on the other hand, are used to obtain reliable estimates from complex datasets in various fields, including biology, economics, and engineering. They provide significant practical advantages in applications such as production forecasting and environmental impact analysis in agriculture. This study investigates the use of machine learning models (ElasticNet, Support Vector Regression, Random Forest, Gradient Boosting, and XGBoost) to estimate the leaf area of faba bean cultivars grown under different shading levels. The study used non-destructive morphological traits, such as plant height, leaf number, and leaf density index, as predictive variables. The dataset, incorporated diverse environmental and biological factors, including varying shading intensities, plant densities, and growth stages across different years. The Support Vector Regression (SVR) model achieved the highest predictive accuracy (R² = 0.92, RMSE = 0.07), significantly outperforming the linear ElasticNet model (R² = 0.76). The proposed model provides a low cost, efficient, and non-destructive approach to leaf area estimation in shaded environments where conventional remote sensing is limited.

Keywords

References

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Details

Primary Language

English

Subjects

Modelling and Simulation, Agricultural Systems Analysis and Modelling, Cereals and Legumes

Journal Section

Research Article

Authors

Murat Kaya ^*
0000-0001-6626-2984
United Kingdom

Osman Varışlı
0000-0003-2090-2207
Türkiye

Abdurrahman Yılmaz
0000-0001-8946-6664
United Kingdom

Ümit Acay This is me
0009-0008-0645-3293
Türkiye

Sibel Ipekeşen This is me
0000-0002-7141-5911
Türkiye

Leonardo Guevara This is me
0000-0002-1581-8943
United Kingdom

Behiye Bicer
0000-0001-8357-8470
Türkiye

Cafer Budak
0000-0002-8470-4579
Türkiye

Ravi Valluru This is me
0000-0001-5725-5766
United Kingdom

Publication Date

March 24, 2026

Submission Date

September 12, 2025

Acceptance Date

December 30, 2025

Published in Issue

Year 2026 Volume: 32 Number: 2

DOI

https://doi.org/10.15832/ankutbd.1782340

IZ

https://izlik.org/JA56LA27JX

Cite

RIS / Bibtex

APA

Kaya, M., Varışlı, O., Yılmaz, A., Acay, Ü., Ipekeşen, S., Guevara, L., Bicer, B., Budak, C., & Valluru, R. (2026). Adaptive Capacity of Machine Learning Approaches for Leaf Area Estimation in Faba Bean Under Diverse Agro-environmental Conditions. Journal of Agricultural Sciences, 32(2), 439-454. https://doi.org/10.15832/ankutbd.1782340

AMA

1.Kaya M, Varışlı O, Yılmaz A, et al. Adaptive Capacity of Machine Learning Approaches for Leaf Area Estimation in Faba Bean Under Diverse Agro-environmental Conditions. J Agr Sci-Tarim Bili. 2026;32(2):439-454. doi:10.15832/ankutbd.1782340

Chicago

Kaya, Murat, Osman Varışlı, Abdurrahman Yılmaz, et al. 2026. “Adaptive Capacity of Machine Learning Approaches for Leaf Area Estimation in Faba Bean Under Diverse Agro-Environmental Conditions”. Journal of Agricultural Sciences 32 (2): 439-54. https://doi.org/10.15832/ankutbd.1782340.

EndNote

Kaya M, Varışlı O, Yılmaz A, Acay Ü, Ipekeşen S, Guevara L, Bicer B, Budak C, Valluru R (March 1, 2026) Adaptive Capacity of Machine Learning Approaches for Leaf Area Estimation in Faba Bean Under Diverse Agro-environmental Conditions. Journal of Agricultural Sciences 32 2 439–454.

IEEE

[1]M. Kaya et al., “Adaptive Capacity of Machine Learning Approaches for Leaf Area Estimation in Faba Bean Under Diverse Agro-environmental Conditions”, J Agr Sci-Tarim Bili, vol. 32, no. 2, pp. 439–454, Mar. 2026, doi: 10.15832/ankutbd.1782340.

ISNAD

Kaya, Murat - Varışlı, Osman - Yılmaz, Abdurrahman - Acay, Ümit - Ipekeşen, Sibel - Guevara, Leonardo - Bicer, Behiye - Budak, Cafer - Valluru, Ravi. “Adaptive Capacity of Machine Learning Approaches for Leaf Area Estimation in Faba Bean Under Diverse Agro-Environmental Conditions”. Journal of Agricultural Sciences 32/2 (March 1, 2026): 439-454. https://doi.org/10.15832/ankutbd.1782340.

JAMA

1.Kaya M, Varışlı O, Yılmaz A, Acay Ü, Ipekeşen S, Guevara L, Bicer B, Budak C, Valluru R. Adaptive Capacity of Machine Learning Approaches for Leaf Area Estimation in Faba Bean Under Diverse Agro-environmental Conditions. J Agr Sci-Tarim Bili. 2026;32:439–454.

MLA

Kaya, Murat, et al. “Adaptive Capacity of Machine Learning Approaches for Leaf Area Estimation in Faba Bean Under Diverse Agro-Environmental Conditions”. Journal of Agricultural Sciences, vol. 32, no. 2, Mar. 2026, pp. 439-54, doi:10.15832/ankutbd.1782340.

Vancouver

1.Murat Kaya, Osman Varışlı, Abdurrahman Yılmaz, Ümit Acay, Sibel Ipekeşen, Leonardo Guevara, Behiye Bicer, Cafer Budak, Ravi Valluru. Adaptive Capacity of Machine Learning Approaches for Leaf Area Estimation in Faba Bean Under Diverse Agro-environmental Conditions. J Agr Sci-Tarim Bili. 2026 Mar. 1;32(2):439-54. doi:10.15832/ankutbd.1782340