Browsing by Author "Ugur, Remzi"

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Refinement of Surface Sterilization Protocol for in Vitro Olive (Olea Europaea L.) Shoot Proliferation and Optimizing by Machine Learning Techniques
(Korean Soc Horticultural Science, 2025) Palaz, Esra Bulunuz; Demirel, Serap; Popescu, Gheorghe Cristian; Demirel, Fatih; Ugur, Remzi; Yaman, Mehmet; Tunc, Yazgan
The olive tree (Olea europaea L.) is one of the most ancient fruit species grown throughout history. Given the challenges and costs associated with propagating olive cultivars by cuttings and grafting, it is crucial to identify a method for efficient and widespread propagation. Micropropagation is especially advantageous for propagating plants that are conventionally challenging to propagate or for producing virus-free seedlings or plants with specified traits. This work aimed to improve the in vitro shoot proliferation of O. europaea L. 'Sultani' cultivated in T & uuml;rkiye. Machine learning (ML) techniques were used to predict the efficiency of surface sterilization treatments. The explants were subjected to varied concentrations and durations of five disinfectants: hydrogen peroxide (H2O2), silver nitrate (AgNO3), mercuric chloride (HgCl2), sodium hypochlorite (NaOCl), and chlorine dioxide (ClO2). Each disinfectant was assigned three treatment levels (T1, T2, T3), which varied in concentration and exposure duration. The measured variables were contamination rate, survival rate, growth rate, shoot diameter, shoot length, and leaf number. ClO2 and NaOCl were the most efficient disinfection agents for the growth of explants. ClO2 showed particularly excellent results in terms of shoot diameter (0.765 mm), shoot length (43.733 mm), and leaf number (14.578). NaOCl treatment resulted in the greatest growth percentage (70.55%). AgNO3 treatment performed moderately performance in most parameters, but the lowest contamination rate (13.556%) was observed. Ultimately, the selection of chemical and treatment techniques substantially impacted the efficacy of in vitro olive shoot proliferation. The support vector regression, random forest, extreme gradient boosting (XGBoost), elastic net, and Gaussian processes algorithms were used to model and forecast the optimal sterilizing settings. The XGBoost provided the most accurate (R2) for survival rate, growth rate, shoot diameter, shoot length, and leaf number variables; 0.587, 0.959, 0.843, 0.894, and 0.900, respectively. The XGBoost algorithm was used to predict and optimize surface sterilization. The optimal circumstances for survival and development were projected to include explants sterilized with a 30% solution of NaOCl for 20 min. Moreover, it was projected that explants treated with a 15% concentration of ClO2 for 30 min would be possibly ideal in terms of shoot diameter, shoot length, and leaf number values. ML algorithms could further optimize these protocols for better outcomes, reducing the number of treatments needed and improving efficiency.
Usage of Machine Learning Algorithms for Establishing an Effective Protocol for the in Vitro Micropropagation Ability of Black Chokeberry (Aronia Melanocarpa (Michx.) Elliott)
(Mdpi, 2023) Demirel, Fatih; Ugur, Remzi; Popescu, Gheorghe Cristian; Demirel, Serap; Popescu, Monica
The primary objective of this research was to ascertain the optimal circumstances for the successful growth of black chokeberry (Aronia melanocarpa (Michx.) Elliott) using tissue culture techniques. Additionally, the study aimed to explore the potential use of machine learning algorithms in this context. The present research investigated a range of in vitro parameters such as total number of roots (TNR), longest root length (LRL), average root length (ARL), number of main roots (NMR), number of siblings (NS), shoot length (SL), shoot diameter (SD), leaf width (LW), and leaf length (LL) for Aronia explants cultivated in different media (Murashige and Skoog (MS) and woody plant medium (WPM)) with different concentrations (0, 0.5, 1, 1.5, and 2 mg L-1) of indole-3-butyric acid (IBA). The study showed that IBA hormone levels may affect WPM properties, affecting the LRL and ARL variables. Aronia explant media treated with 2 mg L-1 IBA had the greatest TNR, NMR, NS, SL, and SD values; 31.67 pieces, 2.37 pieces, 5.25 pieces, 66.60 mm, and 2.59 mm, in that order. However, Aronia explants treated with 1 mg L-1 IBA had the highest LW (9.10 mm) and LL (14.58 mm) values. Finally, Aronia explants containing 0.5 mg L-1 IBA had the greatest LRL (89.10 mm) and ARL (57.57 mm) values. In general, the results observed (TNR, LRL, ARL, NMR, NS, SL, SD, LW, and LL) indicate that Aronia explants exhibit superior growth and development in WPM (25.68 pieces, 68.10 mm, 51.64 mm, 2.17 pieces, 4.33 pieces, 57.95 mm, 2.49 mm, 8.08 mm, and 14.26 mm, respectively) as opposed to MS medium (20.27 pieces, 59.92 mm, 47.25 mm, 1.83 pieces, 3.57 pieces, 49.34 mm, 2.13 mm, 6.99 mm, and 12.21 mm, respectively). In the context of the in vitro culturing of Aronia explants utilizing MS medium and WPM, an analysis of machine learning models revealed that the XGBoost and SVM models perform better than the RF, KNN, and GP models when it comes to making predictions about those variables. In particular, the XGBoost model stood out due to the fact that it had the greatest R-squared value, and showed higher predictive ability in terms of properly forecasting values in comparison to actual outcomes. The findings of a linear regression (LR) analysis were used in order to conduct an efficacy study of the XGBoost model. The LR results especially confirmed the findings for the SD, NS, and NMR variables, whose R-squared values were more than 0.7. This demonstrates the extraordinary accuracy that XGboost has in predicting these particular variables. As a consequence of this, it is anticipated that it will be beneficial to make use of the XGboost model in the dosage optimization and estimation of in vitro parameters in micropropagation studies of the Aronia plant for further scientific investigation.