Artificial neural network and decision tree–based models for prediction and validation of in vitro organogenesis of two hydrophytes—Hemianthus callitrichoides and Riccia fluitans

Özcan, Esra; Atar, Hasan Hüseyin; Ali, Seyid Amjad; Aasım, Muhammad

doi:10.1007/s11627-023-10367-z

Cited by 7 publications

(6 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning algorithms have the potential to be effective and predictive decision-making tools for in vitro plant micropropagation processes because of their ability to forecast and define complex processes involving numerous components [29,30]. However, compared with their widespread application in other scientific fields, the application of ML techniques in the context of plant and agricultural sciences is somewhat limited [31]. Artificial neural networks (ANNs) are a class of nonlinear computing methods used for various tasks such as clustering data, generating predictions, and classifying complex systems [32].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, diverse machine learning models have proven effective in accurately forecasting and refining plant tissue culture procedures. These models have been applied in various investigations, including in vitro mutagenesis, micropropagation, regeneration studies, plant system biology, in vitro organogenesis, stress physiology, and salt stress [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42]. Only a few studies have used machine learning models to examine drought stress responses.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Offers Insights into the Impact of In Vitro Drought Stress on Strawberry Cultivars

Şimşek

2024

Agriculture

View full text Add to dashboard Cite

This study aimed to assess the susceptibility of three strawberry cultivars (“Festival”, “Fortuna”, and “Rubygem”) to drought stress induced by varying polyethylene glycol (PEG) concentrations in the culture medium. Plantlets were cultivated on a solid medium supplemented with 1 mg/L BAP, and PEG concentrations (0, 2, 4, and 6 mg/L) were introduced to simulate drought stress. Morphological changes were observed, and morphometric analysis was conducted. Additionally, artificial neural network (ANN) analysis and machine learning approaches were integrated into this study. The results showed significant effects of PEG concentrations on plant height and multiplication coefficients, highlighting genotype-specific responses. This study employed various machine learning models, with random forest consistently demonstrating superior performance. Our findings revealed the random forest model outperformed others with a remarkable global diagnostic accuracy of 91.164%, indicating its superior capability in detecting and predicting water stress effects in strawberries. Specifically, the RF model excelled in predicting root length and the number of roots for “Festival” and “Fortuna” cultivars, demonstrating its reliability across different genetic backgrounds. Meanwhile, for the “Rubygem” cultivar, the multi-layer perceptron (MLP) and Gaussian process (GP) models showed particular strengths in predicting proliferation and plant height, respectively. These findings highlight the potential of ML models, particularly RF, to enhance agricultural breeding and cultivation strategies through accurate phenotypic predictions, suggesting a promising direction for future research to improve these predictions further. This research contributes to understanding strawberry responses to drought stress and emphasizes the potential of machine learning in predicting plant characteristics.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Offers Insights into the Impact of In Vitro Drought Stress on Strawberry Cultivars

Şimşek

2024

Agriculture

View full text Add to dashboard Cite

show abstract

“…Machine learning algorithms emerge as potent and predictive tools for decision-making in the sector of in vitro plant propagation, due to their proficiency in clarifying and defining the complexity of processes that involve a multitude of factors. Currently, these models have been applied in various in vitro culture investigations, including micropropagation, regeneration and in vitro organogenesis, stress physiology, and salt stress [28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Saffron In Vitro Propagation: An Innovative Method by Temporary Immersion System (TIS), Integrated with Machine Learning Analysis

Tarraf,

İzgü,

Şimşek

et al. 2024

Horticulturae

View full text Add to dashboard Cite

The propagation of Crocus sativus L. relies exclusively on corm multiplication. As underground storage organs, corms are susceptible to a wide range of pathogens, environmental stresses, and diseases, making traditional propagation methods often ineffective with the loss of valuable material. In vitro propagation offers an alternative for the saffron culture under controlled conditions. In particular, the innovative application of the Temporary Immersion System (TIS) represents a technological advancement for enhancing biomass production with a reduction in operational costs. The current study utilized the Plantform™ bioreactor to propagate in vitro saffron corms from the ‘Abruzzo’ region (Italy), integrating machine learning models to assess its performance. The evaluation of saffron explants after 30, 60, and 90 days of culture showed a marked improvement in growth and microcorm production compared to conventional in vitro culture on semisolid medium, supported by the machine learning analysis. Indeed, the Random Forest algorithm revealed a predictive accuracy with an R2 value of 0.81 for microcorm number, showcasing the capability of machine learning models to forecast propagation outcomes effectively. These results confirm that applying TIS in saffron culture could lead to economically viable, large biomass production within a controlled environment, irrespective of seasonality. This study represents the first endeavor to use TIS technology to enhance the in vitro propagation of saffron in conjunction with machine learning, suggesting an innovative approach for cultivating high-value crops like saffron.

show abstract

“…ML is a prominent subfield in artificial intelligence that can predict and classify outcomes based on specific inputs [ 23 , 24 ]. By utilizing ML techniques, computers can autonomously learn and convert data into meaningful knowledge, thus eliminating the need for explicit human programming [ 25 – 27 ].…”

Section: Introductionmentioning

confidence: 99%

Leveraging machine learning to unravel the impact of cadmium stress on goji berry micropropagation

Isak,

Bozkurt,

Tütüncü

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

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.

show abstract

Artificial neural network and decision tree–based models for prediction and validation of in vitro organogenesis of two hydrophytes—Hemianthus callitrichoides and Riccia fluitans

Cited by 7 publications

References 36 publications

Machine Learning Offers Insights into the Impact of In Vitro Drought Stress on Strawberry Cultivars

Machine Learning Offers Insights into the Impact of In Vitro Drought Stress on Strawberry Cultivars

Saffron In Vitro Propagation: An Innovative Method by Temporary Immersion System (TIS), Integrated with Machine Learning Analysis

Leveraging machine learning to unravel the impact of cadmium stress on goji berry micropropagation

Contact Info

Product

Resources

About