首页|Researcher from Erciyes University Reports Recent Findings in Machine Learning ( Machine Learning Offers Insights into the Impact of In Vitro Drought Stress on S trawberry Cultivars)
Researcher from Erciyes University Reports Recent Findings in Machine Learning ( Machine Learning Offers Insights into the Impact of In Vitro Drought Stress on S trawberry Cultivars)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on artificial in telligence have been published. According to news reporting from Erciyes Univers ity by NewsRx journalists, research stated, "This study aimed to assess the susc eptibility 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 wi th 1 mg/L BAP, and PEG concentrations (0, 2, 4, and 6 mg/L) were introduced to s imulate drought stress." Financial supporters for this research include Erciyes University Scientific Pro jects Units. The news correspondents obtained a quote from the research from Erciyes Universi ty: "Morphological changes were observed, and morphometric analysis was conducte d. Additionally, artificial neural network (ANN) analysis and machine learning a pproaches were integrated into this study. The results showed significant effect s of PEG concentrations on plant height and multiplication coefficients, highlig hting 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 capa bility in detecting and predicting water stress effects in strawberries. Specifi cally, the RF model excelled in predicting root length and the number of roots f or ‘Festival' and ‘Fortuna' cultivars, demonstrating its reliability across diff erent genetic backgrounds. Meanwhile, for the ‘Rubygem' cultivar, the multi-laye r perceptron (MLP) and Gaussian process (GP) models showed particular strengths in predicting proliferation and plant height, respectively."
Erciyes UniversityCyborgsEmerging Te chnologiesMachine Learning
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