This study investigates the effects of an AI-supported irrigation system on the production of natural plant species and irrigation efficiency at Rize Recep Tayyip Erdoğan University. To enhance water resource efficiency while utilizing Turkey’s rich plant diversity, Geranium psilostemon Ledeb. (Black-Eyed Crane’s-Bill) was selected for cultivation. The research includes adaptation trials and growth monitoring of this perennial taxon, which naturally grows at an altitude of 2000 m. The experiments were conducted in two different environments: one utilizing an AI-supported irrigation system and the other relying on manual irrigation. The findings reveal that AI-supported irrigation systems optimize irrigation strategies, providing a more efficient and effective plant cultivation process compared to manual irrigation. The AI-supported irrigation system continuously monitors air and soil moisture levels, ensuring optimal irrigation conditions and instant adaptation to seasonal variations. This innovative approach minimizes water losses while preventing soil salinization, thereby offering a significant solution for sustainable agricultural practices. In conclusion, this study demonstrates that natural plant species can be effectively cultivated using AI-supported irrigation systems and that these systems hold great potential for water conservation and ecological balance. These findings present a crucial step toward developing effective solutions for global water challenges and promoting sustainable landscape and agricultural practices.

Urbanization and the reduction of green spaces have significantly contributed to problems such as rising temperatures and declining air quality in urban areas. This study examines the impact of different types of green areas—broadleaved trees, coniferous trees, shrubs, and vines—on urban temperature regulation at the Recep Tayyip Erdoğan University Zihni Derin Campus. Surface temperature, humidity, ambient temperature, and wind speed measurements were collected using an infrared thermometer over a one-year period under various climatic conditions (August, October, January, and April) and at different times of the day (09:00 AM, 01:00 PM, and 05:00 PM). To quantitatively assess the cooling effect of each type of green area, a Response Surface Methodology (RSM) was applied, and a predictive formula was developed to estimate the cooling impact of various green areas under different environmental conditions. These formulated models enable the estimation of the temperature reduction provided by these four plant types based on different input parameters, achieving an accuracy of approximately 92% or higher without requiring direct measurements. The findings of this study provide a robust methodological framework and a practical tool for optimizing green space designs, mitigating urban heat island effects, and enhancing urban living comfort under various climatic conditions.

This study aimed to evaluate the rooting success of Loropetalum chinense var. rubrum Yieh cuttings in three different rooting media: 100% peat, 100% perlite, and a 50% peat–50% perlite mixture. Additionally, three concentrations of Indole Butyric Acid (IBA)—1000 ppm, 3000 ppm, and 6000 ppm—were tested, along with a control group consisting of non-hormone-treated cuttings. The chlorophyll content of the leaves was measured in µmol/m², and its relationship with rooting success was examined. Measurements were conducted every 15 days over a 120-day period. The collected data were analyzed using both an artificial neural network (ANN) and SPSS 29.0.2 statistical software. Results indicated that perlite medium yielded the highest rooting rate and chlorophyll concentration, whereas the peat medium performed the poorest. While 1000 ppm IBA led to the greatest improvement in rooting rate, 6000 ppm resulted in the highest chlorophyll concentration. The highest chlorophyll levels were observed during measurement periods M7, M8, and M9. Analyses of peat moisture and pH indicated that the physicochemical properties of the rooting media significantly influenced cutting development. This study aims to support the identification of optimal propagation methods for this species and to contribute to the literature by developing an ANN model based on the measured parameters.

This study aimed to evaluate and optimize the effects of three auxin types—indole-3-butyric acid (IBA), naphthaleneacetic acid (NAA), and indole-3-acetic acid (IAA)—applied at four concentrations (1000, 3000, 5000, and 8000 ppm) on the rooting performance of Photinia × fraseri Dress. stem cuttings. The experiment was conducted under controlled greenhouse conditions using a sterile perlite medium. Rooting trays were placed on bottom-heated propagation benches maintained at a set temperature of 25 ± 2 °C to stimulate root formation. However, the actual rooting medium temperature—measured manually every four days from the perlite zone using a calibrated thermometer—ranged between 18 °C and 22 °C, with an overall average of approximately 20 ± 2 °C. The average values of these root-zone temperatures were used in the statistical analyses. Rooting percentage, root number, root length, callus formation, and mortality rate were recorded after 120 days. In addition to classical one-way ANOVA, response surface methodology (RSM) was employed to model and optimize the interactions between auxin type, concentration, and temperature. The results revealed that 5000 ppm IBA significantly enhanced rooting performance, yielding the highest rooting percentage (85%), average root number (5.80), and root length (6.30 cm). RSM-based regression models demonstrated strong predictive power, with the model for rooting percentage explaining up to 92.79% of the total variance. Temperature and auxin concentration were identified as the most influential linear factors, while second-order and interaction terms—particularly T·ppm—contributed substantially to root length variation. These findings validate IBA as the most effective exogenous auxin for the vegetative propagation of Photinia × fraseri Dress. and provide practical recommendations for optimizing hormone treatments. Moreover, the study offers a robust statistical modeling framework that can be applied to similar propagation systems in woody ornamental plants.

This study investigates the influence of varying cold stratification durations (0–165 days) on the germination performance and early seedling development of Rudbeckia fulgida. Seeds were divided into 11 groups at 15-day intervals, using a total of 1320 seeds. For each stratification duration, an equivalent number of seeds stored at room temperature served as non-stratified controls. Results demonstrated a clear and significant increase in germination percentage with longer stratification periods (Kruskal–Wallis, H = 57.03, p < 0.001), with the highest germination observed at 135 and 165 days (96.7%). In contrast, seeds kept at room temperature exhibited low and inconsistent germination. Strong positive correlations were detected between stratification duration and both germination percentage (r = 0.914) and post-stratification seed weight (r = 0.419). Furthermore, a Response Surface Methodology (RSM) model was developed to predict germination behavior, achieving an exceptionally high 99% predictive accuracy. The RSM analysis confirmed that cold stratification duration is the dominant factor shaping germination responses in Rudbeckia fulgida Aiton. Overall, the study demonstrates that cold stratification is essential for breaking seed dormancy in R. fulgida, substantially improving propagation efficiency and offering valuable insights for nursery production, landscape practices, and restoration ecology.

Limited information is available on how vermicompost form, form-specific dose, and application schedule jointly affect tea seedling development. This 135-day humid outdoor nursery study evaluated solid and liquid vermicompost applied at two doses as either single or three split applications to 270 individually potted Camellia sinensis seedlings assigned to eight factorial treatments and an untreated control. Repeated measurements of seedling height, root collar diameter, and leaf chlorophyll content were analyzed using linear mixed-effects models fitted by restricted maximum likelihood (REML); the control was retained only in supplementary one-way ANOVAs. Soil pH, electrical conductivity (EC), and temperature were analyzed by two-way ANOVA. Vermicompost form, measurement time, and their interaction significantly affected seedling height and root collar diameter. Application schedule significantly affected root collar diameter (F = 7.35, p = 0.007) and chlorophyll content (F = 19.74, p < 0.001), but not seedling height, whereas dose within form showed no significant main effect on any of these traits. The form × application schedule × time interaction was significant for seedling height, whereas dose within form × application schedule × time was significant for root collar diameter and chlorophyll content. Treatment differences generally emerged from day 30 onward. The 30 g solid vermicompost treatment applied in three equal doses produced the highest final seedling height (24.80 cm) and root collar diameter (14.00 mm) and the highest chlorophyll content recorded during the experiment (914.98 µmol m⁻² on day 75). Treatment, measurement time, and their interaction significantly affected soil pH, EC, and temperature; pH remained within 4.90–5.76, and EC values did not indicate salinity stress. Selected split applications of solid vermicompost therefore showed potential to improve tea seedling growth, but significant higher-order interactions demonstrated that responses depended on vermicompost form, dose within form, application schedule, trait, and measurement time.