In this study, we refine in vitro propagation techniques for Camellia sinensis using a machine learning approach to ascertain the influence of different shooting and rooting conditions on key growth metrics. This was achieved by applying random forest (RF), XGBoost, and multilayer perceptron (MLP) models to dissect the complexities of micropropagation and rooting processes. The research unveiled significant disparities in growth metrics under varying media conditions, underscoring the profound impact of media composition on plant development. The meticulous statistical analysis, employing ANOVA, highlighted statistically significant differences in growth metrics, indicating the critical role of media composition in optimizing growth conditions. Methodologically, the study utilized explants from 2–3-year-old tea plants, which underwent sterilization before being introduced to two distinct culture media for their micropropagation and rooting phases. Statistical analyses were conducted to evaluate the differences in growth outcomes between media, while machine learning models were employed to predict the efficacy of micropropagation and rooting based on various growth regulators. This approach allowed for a comprehensive evaluation of the model’s performance in simulating plant growth under different conditions, leveraging metrics like R², RMSE, and MAE. The findings from this study significantly advance the understanding of tea plant micropropagation, highlighting the utility of machine learning models in agricultural optimization. This research contributes to enhancing micropropagation strategies for the tea plant and exemplifies the transformative potential of integrating machine learning into plant science, paving the way for improved agricultural and horticultural practices. This interdisciplinary approach offers a novel perspective on optimizing in vitro propagation processes, contributing substantially to plant tissue culture and biotechnology.

This study investigates the influence of cadmium (Cd) stress on the micropropagation of Goji Berry (Lycium barbarum L.) across three distinct genotypes (ERU, NQ1, NQ7), employing an array of machine learning (ML) algorithms, including Multilayer Perceptron (MLP), Support Vector Machines (SVM), Random Forest (RF), Gaussian Process (GP), and Extreme Gradient Boosting (XGBoost). The primary motivation is to elucidate genotype-specific responses to Cd stress, which poses significant challenges to agricultural productivity and food safety due to its toxicity. By analyzing the impacts of varying Cd concentrations on plant growth parameters such as proliferation, shoot and root lengths, and root numbers, we aim to develop predictive models that can optimize plant growth under adverse conditions. The ML models revealed complex relationships between Cd exposure and plant physiological changes, with MLP and RF models showing remarkable prediction accuracy (R² values up to 0.98). Our findings contribute to understanding plant responses to heavy metal stress and offer practical applications in mitigating such stress in plants, demonstrating the potential of ML approaches in advancing plant tissue culture research and sustainable agricultural practices.

Pomegranate (Punica granatum) is a perennial herb with its long history and has been the symbol of health, fertility and spirituality. In addition to being consumed as fresh fruit, pomegranate is consumed as pomegranate juice, syrup, pomegranate syrup and many different product groups. In particular, pomegranate seeds, which are often produced as waste, are evaluated separately in terms of their fatty acid composition and antioxidant capacity. In this study, the fatty acid composition of pomegranate seed oils was determined by gas chromatography/mass spectrophotometry (GC/MS). Total antioxidant activity analysis was performed using DPPH (2, 2-diphenyl-1-picrylhydrazyl) method and antioxidant activity value was determined as trolox equivalent (TE). In the study, it was determined that the antioxidant capacity was 171.44 mg TE / 100g. According to GC / MS analysis, it was determined that the most dominant fatty acid was punicic Acid (68.12%). Oleic acid (8.51%), linoleic acid (6.71%) and palmitic acid (5.55%) are the highest fatty acids after punicic acid. It is clear that apart from the widespread consumption of pomegranate fruit, which is very beneficial for health, its seeds can also be used.

Pitaya belongs to the family Cactaceae and the genus Hylocereus. It is essential todevelop tissue culture protocols according to the appropriate variety to spread pitayacommercial production and ensure healthy sapling production. This study aimed todetermine the best plant growth regulators (PGRs) in the micropropagation process,and their effects on different pitaya cultivars were evaluated. Shoots of different pitayacultivars were cultured in Murashige and Skoog (MS) basal medium supplementedwith Indole-3-butyric (IBA), 6-benzyl amino purine (BAP), and gibberellic acid(GA3). The highest micropropagation coefficient was determined in Physical Graffitivariety cultured in MS medium supplemented with 2 mg/L BAP. The general evaluation based on the variety determined that the best micropropagation was in the RoyalRed variety. In in vitro rooting studies, the best rooting variety was Royal Red(54.47%), followed by Siam Red (50.33%), Physical Graffiti (47.75%), and SeoulKitchen (44.82%). It was determined that the Royal Red variety is grown in R2 (MSmedium supplemented with 1 mg/l IBA) medium gave the best results in all criteriaof shoot length (4.28 cm), root length (6.45 cm), and root formation on the face(74.48%). It was envisioned that these differences between the micropropagation,growth, and rooting of the cultivars used in the studies resulted from the cultivar characteristics.Keywords: Pitaya, In vitro, Micropropagation, Royal Red, Seoul Kitchen, Siam Red,Physical Graffiti

Banana is an important nutritional source for millions of people because it contains protein, carbohydrates, vitamins and minerals. Therefore, the need for banana production is increasing day by day. Various methods are being developed to meet this need. Among these methods, biotechnological applications are becoming more prevalent over time. In micropropagation of in vitro plantlets, which forms the basis of banana production and is one of the most important stages, the methods used and banana varieties must be selected correctly. Therefore, it is very important to decide which protocol is suitable for which variety. In TIBs, in vitro micropropagation and rooting were compared with solid culture by adjusting the frequency of immersion of in vitro plantlets into the nutrient medium and the contact time of nutrients to the plantlets in the immersion medium. It was determined that the TIB application was more successful than the solid medium experiment in many aspects, especially the micropropagation rate (4.20) and rooting rate (84%).