Machine Learning-Based Evaluation of Nutrient Distribution in Grafted Cucumber Plants

Research Article

Authors

DOI:

https://doi.org/10.21276/pt.2025.v2.i3.3

Keywords:

Cucumber, Grafting, Rootstock, Machine learning, Artificial Intelligence

Abstract

This study investigated the effects of different grafting combinations on nutrient (Mg, S, Fe, B) accumulation and transport in cucumber plants, combining conventional analyses with machine learning. Greenhouse experiments compared non-grafted, self-grafted, and rootstock-grafted (TZ148, Cremna, Maximus) plants. Self-grafted plants accumulated the highest root Mg (0.095 µg/g), while non-grafted plants retained excessive Fe in roots (15.80 µg/g). In contrast, Maximus rootstock reduced root Fe (7.68 µg/g) and enhanced fruit Mg (0.414 µg/g), Fe (22.91 µg/g), and B (3.63 µg/g), while Cremna promoted higher S in fruit (0.640 µg/g). Random Forest models classified grafting combinations based on tissue nutrient profiles, identifying S, B, and Mg as the most discriminative features, supported by SHAP and PCA analyses. Overall, results show that grafting—especially with Maximus and Cremna rootstocks—optimizes nutrient allocation and fruit mineral quality. Integrating machine learning with physiological data provides a robust approach for improving rootstock-scion selection in sustainable cucumber production.

Author Biographies

  • Seher Toprak, Hatay Mustafa Kemal University, Hatay, Türkiye

     Department of Horticulture 

  • Ömer Faruk Coşkun, Hatay Mustafa Kemal University, Hatay, Türkiye

    Associate Professor, Faculty of Agriculture, Department of Horticulture 

References

Singh H, Kumar P, Kumar A, Kyriacou MC, Colla G, Rouphael Y. Grafting tomato as a tool to improve salt tolerance. Agronomy. 2020; 10:263. https://doi.org/10.3390/agronomy10020263

Coskun OF. Effects of grafting on genomic stability in salinity stress conditions in cucumber (Cucumis sativus L.). Erciyes Tarım ve Hayvan Bilimleri Dergisi. 2023a; 6(1):23-30. https://doi.org/10.55257/ethabd.1231233

Coskun OF. The effect of grafting on morphological, physiological and molecular changes induced by drought stress in cucumber. Sustainability. 2023b; 15:875. https://doi.org/10.3390/su15010875

Coskun OF, Toprak S. Determination of the effect of cucumber grafting on some morphological and physiological characteristics in hydroponic conditions. Int J Agric Environ Food Sci. 2023; 7(1):163-170. https://doi.org/10.31015/jaefs.2023.1.20

Turhan A, Ozmen N, Serbeci MS, Seniz V. Effects of grafting on different rootstocks on tomato fruit yield and quality. Hortic Sci. 2011; 38:142-149. https://doi.org/10.17221/51/2011-HORTSCI

Bletsos F, Olympios C. Rootstocks and grafting of tomatoes, peppers, and eggplants for soil-borne disease resistance, improved yield, and quality. Eur J Plant Sci and Biotech. 2008; 2:62-73.

Savvas D, Papastavrou D, Ntatsi G, Ropokis A, Olympios C, Hartmann H, Schwarz D. Interactive effects of grafting and Mn-supply level on growth, yield, and nutrient uptake by tomato. Hort Sci. 2009;44:1978-1982.

Colla G, Suãrez CMC, Cardarelli M, Rouphael Y. Improving nitrogen use efficiency in melon by grafting. HortScience. 2010; 45:559-565. https://doi.org/10.21273/HORTSCI.45.4.559

Bekhradi F, Kashi A, Delshad M. Effect of three cucurbit rootstocks on vegetative and yield of ‘Charleston Grey’ watermelon. Int J Plant Prod. 2011; 5(2):105-109. https://doi.org/10.22069/ijpp.2012.724

Khah EM. Effect of grafting on growth, performance, and yield of aubergine (Solanum melongena L.) in greenhouse and open-field. Int J Plant Prod. 2011; 5(4):359-366. https://doi.org/10.22069/ijpp.2012.746

Liakos KG, Busato P, Moshou D, Pearson S, Bochtis D. Machine learning in agriculture: A review. Sensors. 2018;18(8):2674. https://doi.org/10.3390/s18082674

Kamilaris A, Prenafeta-Boldú FX. Deep learning in agriculture: A survey. Comput Electron Agric. 2018; 147:70-90. https://doi.org/10.1016/j.compag.2018.02.016

Yang F, Zhou Y, Du J, Wang K, Lv L, Long W. Prediction of fruit characteristics of grafted plants of Camellia oleifera by deep neural networks. Plant Methods. 2024; 20, 23. https://doi.org/10.1186/s13007-024-01145-y

Sun S, Tian YS, Leng D, Li X, Yuan SL, Xing GM. Effects of different kinds of rootstocks on economic yields and mineral nutrition contents of leaves of grafted watermelon seedlings. Plant Nutr Fert Sci.. 2010; 16(1): 179-184. https://doi.org/10.11674/zwyf.2010.0126

Ruiz JM, Belakbir A, López-Cantarero I, Romero L. Leaf macronutrient content and yield in grafted melon plants: A model to evaluate the influence of rootstock genotype. Sci Hortic. 1997; 71:227-234. https://doi.org/10.1016/S0304-4238(97)00106-4

Savvas D, Colla G, Rouphael Y, Schwarz D. Amelioration of heavy metal and nutrient stress in fruit vegetables by grafting. Sci Hortic. 2010; 127:156-161. https://doi.org/10.1016/j.scienta.2010.09.011

Uygur V, Yetişir H. Effect of rootstocks on some growth parameters, phosphorus, and nitrogen uptake by watermelon under salt stress. J Plant Nutr. 2009; 32:629-643. https://doi.org/10.1080/01904160802715448

Salehi R, Kashi A, Lee JM, Babalar M, Delshad M, Lee SG, Huh YC. Leaf gas exchanges and mineral ion composition in xylem sap of Iranian melon affected by rootstocks and training methods. Hortic Sci. 2010; 45:766-770. https://doi.org/10.21273/HORTSCI.45.5.766

Toprak S, Coşkun ÖF. Effects of zinc stress on sugar composition in cucumber (Cucumis sativus L.) organs and prediction of fruit sugar interrelationships using machine learning. In: 22th International Istanbul Scientific Research Congress on Life, Engineering, Architecture and Mathematical Sciences — Congress Proceedings Book. Istanbul, Türkiye; 2025. ISBN: 978-9952-8589-6-9.

Coşkun ÖF. Interactive effects of drought and shading on morphological and physiological traits in melon (Cucumis melo L.) evaluated with machine learning approaches. In: 22th International Istanbul Scientific Research Congress on Life, Engineering, Architecture and Mathematical Sciences — Congress Proceedings Book. Istanbul, Türkiye; 2025. ISBN: 978-9952-8589-6-9.

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Published

2025-10-15

Data Availability Statement

All data generated are included in this article.

Issue

Vol. 2 No. 3 (2025): 2025, Quarter 3

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Articles

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Copyright (c) 2025 Seher Toprak, Ömer Faruk Coşkun (Author)

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How to Cite

1.
Toprak S, Coşkun Ömer F. Machine Learning-Based Evaluation of Nutrient Distribution in Grafted Cucumber Plants: Research Article. phytoTalks. 2025;2(3):457-466. doi:10.21276/pt.2025.v2.i3.3