This study comprehensively explored the micropropagation and rooting capabilities of four distinct lavender genotypes, utilizing culture media with and without 2 g/L of activated charcoal. A systematic examination of varying concentrations of BAP for micropropagation and IBA for rooting identified an optimal concentration of 1 mg/L for both BAP and IBA, resulting in excellent outcomes. Following robust root development, the acclimatization of plants to external conditions achieved a 100% survival rate across all genotypes. In addition to the conventional techniques employed, integrating machine learning (ML) methodologies holds promise for further enhancing the efficiency of lavender propagation protocols. Using cutting-edge computational tools, including MLP, RBF, XGBoost, and GP algorithms, our findings were rigorously examined and forecast using three performance measures (RMSE, R², and MAE). Notably, the comparative evaluation of different machine learning models revealed distinct R2 rates for plant characteristics, with MLP, RBF, XGBoost, and GP demonstrating varying degrees of effectiveness. Future studies may leverage ML models, such as XGBoost, MLP, RBF, and GP, to fine-tune specific variables, including culture media composition and growth regulator treatments. The adaptability and ability of ML techniques to analyze complex biological processes can provide valuable insights into optimizing lavender micropropagation on a broader scale. This collaborative approach, combining traditional in vitro techniques with machine learning, validates the success of current micropropagation and rooting protocols and paves the way for continuous improvement. By embracing ML in lavender propagation studies, researchers can contribute to advancing sustainable and efficient plant propagation techniques, thereby fostering the preservation and exploitation of genetic resources for conservation and agriculture.

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.

A successful regeneration protocol was developed for micropropagation of Lilium akkusianum R. Gämperle, an endemic species of Türkiye, from scale explants. The study also aimed to evaluate the effects of Meta-Topolin (mT) and N6-Benzyladenine (BA) on in vitro regeneration. The Murashige and Skoog medium (MS) supplemented with different levels of α-naphthaleneacetic acid (NAA)/BA and NAA/mT were used for culture initiation in the darkness. The highest callus rates were observed on explants cultured on MS medium with 2.0 mg/L NAA + 0.5 mg/L mT (83.31%), and the highest adventitious bud number per explant was 4.98 in MS medium with 0.5 mg/L NAA + 1.5 mg/L mT. Adventitious buds were excised and cultured in 16/8 h photoperiod conditions. The highest average shoot number per explant was 4.0 in MS medium with 2.0 mg/L mT + 1.0 mg/L NAA. Shoots were rooted with the highest rate (90%) in the medium with the 1.0 mg/L IBA, and the highest survival rate (87.5%) was recorded in rooted shoots in the same medium. The ISSR marker system showed that regenerated plantlets were genetically stable. Besides traditional tissue culture techniques used in the current study, the potential for improving the effectiveness of L. akkusianum propagation protocols by incorporating machine learning methodologies was evaluated. ML techniques enhance lily micropropagation by analyzing complex biological processes, merging with traditional methods. This collaborative approach validates current protocols, allowing ongoing improvements. Embracing machine learning in endemic L. akkusianum studies contributes to sustainable plant propagation, promoting conservation and responsible genetic resource utilization in agriculture.

Protein hydrolysates (PHs) derived from waste materials are promising for sustainable practices in agricultural production. This study evaluated the effects of PH enzymatically derived from anchovy by-products on the root system architecture (RSA) and aboveground development of potted primrose. The plants were treated with 0.5, 1.0, and 1.5 g/L concentrations of PH by drenching with 100 mL/pot at two-week intervals and irrigated once a week with 100 mL/pot during winter and twice weekly during spring. The results revealed that the 1.5 g/L treatment statistically significantly improved dry weight and leaf area, while the highest leaf chlorophyll content was observed with the 1.0 g/L treatment. The treatments did not influence leaf and flower numbers. Treatment with 1.0 g/L produced the most substantial improvement in root surface area, projected area, volume, length, tips, and forks. Additionally, the study employed machine learning (ML) algorithms, including GP, RF, XGBoost, and an ANN-based MLP. The input variables (root surface area, projected area, volume, length, tips, and forks) were assessed to model and predict the root traits. The ML and ANN algorithms’ R-squared rates were noted in the following order: MLP > GP > RF > XGBoost. These outcomes hold significant implications for enhancing primrose growth.

This study investigated the effects of cadmium (Cd) stress on the micropropagation and rooting dynamics of two myrtle (Myrtus communis L.) genotypes with different fruit colors under controlled in vitro conditions. We evaluated the response of these genotypes to varying concentrations of Cd (0, 100, 200, 300, 400, and 500 µM) to determine dose-dependent effects on plantlet multiplication and root formation. Our results demonstrate that the white-fruited (WF) genotype exhibits greater resilience than the black-fruited (BF) genotype across all concentrations, maintaining higher multiplication rates and shoot heights. For instance, the multiplication rate at 100 µM Cd was highest for WF at 6.73, whereas BF showed the lowest rate of 1.94 at 500 µM. Similarly, increasing Cd levels significantly impaired root length and the number of roots for both genotypes, illustrating the detrimental impact of Cd on root system development. Additionally, this study incorporated machine learning (ML) models to predict growth outcomes. The multilayer perceptron (MLP) model, including random forest (RF) and XGBoost, was used to analyze the data. The MLP model performed notably well, demonstrating the potential of advanced computational tools in accurately predicting plant responses to environmental stress. For example, the MLP model accurately predicted shoot height with an R² value of 0.87 and root length with an R² of 0.99, indicating high predictive accuracy. Overall, our findings provide significant insights into the genotypic differences in Cd tolerance and the utility of ML models in plant science. These results underscore the importance of developing targeted strategies to enhance plant resilience in contaminated environments.

The obtention of homozygous lines through in situ parthenogenesis via gamma irradiation method is a comprehensive application in vegetables. However, there are a limited number of studies on in situ parthenogenesis in ornamentals. Therefore, in situ parthenogenetic capacity of more species needs to be examined. For this purpose, the effects of pollination with gamma-irradiated pollen on in vitro ovule cultured in Cyclamen persicum L. were evaluated in this study. Flower buds were collected before anthesis and irradiated at different doses 50, 100, 150, 200, 300, and 450 Gy of gamma-ray using a Co-60 source. The control group was pollinated with non-irradiated pollen. Fruits were harvested 30 days after pollination, and isolated ovule explants were cultured on four different mediums in in vitro. M0 was control group containing half-strength MS basal media; M1 media additionally has 10 g/L maltose, 1.0 g/L proline, 2.0 g/L peptone, 200.0 mg/L spermidine and 0.5 mg/L kinetin; in addition to basal medium, M2 media additionally contains 10 g/L maltose, 1.0 g/L proline, 2.0 g/L peptone, 200.0 mg/L spermidine, 0.4 mg/L gibberellic acid (GA3) and 0.4 mg/L N6-benzyl adenine (BA). M3 media additionally contains 2.0 mg/L 2,4-D and 0.8 mg/L 6-( γ, γ-dimethylallylamino) purine (2iP). Plantlets started to form 10-12 weeks after the beginning of culture. The effects of nutrient media, irradiation dose, and their interactions on plant formation were statistically significant. The lowest plantlet regeneration (0.33%) was obtained from ovule explants exposed to 50 Gy gamma rays and cultured on M3 media while the highest plantlet regeneration (2.66%) was obtained from ovule explants cultured on M1 media 30 days after pollination with non-irradiated pollen grains. According to stomatal observations, there were no statistical differences between donor plant and in vitro regenerated plantlets.