Browsing by Author "Popescu, Gheorghe Cristian"

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Effect of Rhizobacteria Application on Nutrient Content, Bioactive Compounds, Antioxidant Activity, Color Properties and Fruit Characteristics of Strawberry Cultivars
(Mdpi, 2024) Elikara, Alper Umut; Popescu, Gheorghe Cristian; Demirel, Serap; Sumbul, Ahmet; Yaman, Mehmet; Demirel, Fatih; Gunes, Adem
The aim of this study was to determine the effects of single and combined applications of plant-growth-promoting rhizobacteria (PGPR) bacteria on plant nutrition, biochemical content and fruit characteristics in Albion and Monterey strawberry cultivars. Bacillus subtilis OSU-142, Bacillus megaterium M3 and Paenibacillus polymyx were the PGPR used in the experiment. For each bacterial treatment, 10 mL of a 108 CFU mL-1 suspension was applied to the soil where Albion and Monterey cultivars were grown. PGPR bacteria were applied as single treatments and a mixture of equal amounts of these three bacterial species was applied as a mixed treatment. This study was carried out with a total of four different bacterial treatments and one control group. The highest fruit weight was obtained in the Monterey cultivar with 12.67 g in the Mix treatment and in the Albion cultivar with 11.79 g in the Bacillus megaterium M3 treatment. Regarding biochemical properties, Paenibacillus polymyxa was effective in influencing nutrient element content in fruits, while Bacillus subtilis OSU-142, Paenibacillus polymyxa and Bacillus megaterium M3 applications were more effective in leaf nutrient element content. It has been observed that the Mix treatment resulting from the combined use of bacteria, rather than their separate use, has a greater impact on fruit weight. Consequently, it has been understood that PGPR bacteria are potentially effective in improving the agronomic, pomological, and biochemical characteristics of strawberry cultivars and can be used in studies and breeding programs aimed at increasing strawberry yield and quality.
Morpho-Genetic Characterization of Fig (Ficus Carica Var. Rupestris (Hausskn.) Browicz) Genotypes To Be Used as Rootstock
(Springer, 2024) Yildiz, Ercan; Aglar, Erdal; Sumbul, Ahmet; Yaman, Mehmet; Caliskan, Oguzhan; Popescu, Gheorghe Cristian; Gonultas, Metin
With its enormous genetic pool, Turkiye is the homeland of the fig and many plant species. The common fig species in the country's natural population are Ficus carica var. caprificus (male figs), Ficus carica var. domestica (female figs; edible figs), and Ficus carica var. rupestris. In this study, the morphological and molecular characterization of 42 genotypes, including those obtained by selection from the Ficus carica var. rupestris (Hausskn.) Browicz population, which is naturally spread in a limited area in Tunceli province, was performed. This study evaluated the qualitative and quantitative characteristics of 23 fig genotypes. These results showed that tree growth habit, lateral shoot formation, apical dominancy, and leaf length characteristics were highly discriminant variables for phenotypic description in wild fig genotypes. The genetic relationship between the genotypes was demonstrated with 12 SRAP and 9 ISSR primers. As a result of the study, it was determined that the genetic similarity values ranged between 0.52 and 0.94. It was determined that the first five essential components (PCA) contributed 20.87%, 13.21%, 10.66%, 9.58%, and 7.11% of the total variation, respectively, and their cumulative rate corresponded to 61.43% of the total variation. Very detailed results on the genetic variation in the fig population in the region were obtained by morphological features and molecular methods. The seven genotypes selected were propagated to determine their potential for dwarf rootstocks. The results of the present study may provide significant leads for further research on this subject. The potential of dwarf rootstocks in figs can be an essential tool for modern fruit growing.
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.