Optimizing Micropropagation and Rooting Protocols for Diverse Lavender Genotypes: A Synergistic Approach Integrating Machine Learning Techniques
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 a...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Horticulturae |
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| Online Access: | https://www.mdpi.com/2311-7524/10/1/52 |
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| author | Özhan Şimşek Akife Dalda Şekerci Musab A. Isak Fatma Bulut Tolga İzgü Mehmet Tütüncü Dicle Dönmez |
| author_facet | Özhan Şimşek Akife Dalda Şekerci Musab A. Isak Fatma Bulut Tolga İzgü Mehmet Tütüncü Dicle Dönmez |
| author_sort | Özhan Şimşek |
| collection | DOAJ |
| description | 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 (<i>RMSE</i>, <i>R</i><sup>2</sup>, and <i>MAE</i>). 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. |
| first_indexed | 2024-03-08T10:49:56Z |
| format | Article |
| id | doaj.art-fa7ced2a536640ce880a624ed2d2bc4e |
| institution | Directory Open Access Journal |
| issn | 2311-7524 |
| language | English |
| last_indexed | 2024-03-08T10:49:56Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Horticulturae |
| spelling | doaj.art-fa7ced2a536640ce880a624ed2d2bc4e2024-01-26T16:49:48ZengMDPI AGHorticulturae2311-75242024-01-011015210.3390/horticulturae10010052Optimizing Micropropagation and Rooting Protocols for Diverse Lavender Genotypes: A Synergistic Approach Integrating Machine Learning TechniquesÖzhan Şimşek0Akife Dalda Şekerci1Musab A. Isak2Fatma Bulut3Tolga İzgü4Mehmet Tütüncü5Dicle Dönmez6Horticulture Department, Agriculture Faculty, Erciyes University, 38030 Kayseri, TürkiyeHorticulture Department, Agriculture Faculty, Erciyes University, 38030 Kayseri, TürkiyeAgricultural Sciences and Technology Department, Graduate School of Natural and Applied Sciences, Erciyes University, 38030 Kayseri, TürkiyeHorticulture Department, Agriculture Faculty, Erciyes University, 38030 Kayseri, TürkiyeInstitute of BioEconomy, National Research Council of Italy (CNR), 50019 Florence, ItalyDepartment of Horticulture, University of Ondokuz Mayıs, 55200 Samsun, TürkiyeBiotechnology Research and Application Center, Çukurova University, 01330 Adana, TürkiyeThis 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 (<i>RMSE</i>, <i>R</i><sup>2</sup>, and <i>MAE</i>). 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.https://www.mdpi.com/2311-7524/10/1/52micropropagationrooting efficiencyactivated carbonBAPIBA |
| spellingShingle | Özhan Şimşek Akife Dalda Şekerci Musab A. Isak Fatma Bulut Tolga İzgü Mehmet Tütüncü Dicle Dönmez Optimizing Micropropagation and Rooting Protocols for Diverse Lavender Genotypes: A Synergistic Approach Integrating Machine Learning Techniques Horticulturae micropropagation rooting efficiency activated carbon BAP IBA |
| title | Optimizing Micropropagation and Rooting Protocols for Diverse Lavender Genotypes: A Synergistic Approach Integrating Machine Learning Techniques |
| title_full | Optimizing Micropropagation and Rooting Protocols for Diverse Lavender Genotypes: A Synergistic Approach Integrating Machine Learning Techniques |
| title_fullStr | Optimizing Micropropagation and Rooting Protocols for Diverse Lavender Genotypes: A Synergistic Approach Integrating Machine Learning Techniques |
| title_full_unstemmed | Optimizing Micropropagation and Rooting Protocols for Diverse Lavender Genotypes: A Synergistic Approach Integrating Machine Learning Techniques |
| title_short | Optimizing Micropropagation and Rooting Protocols for Diverse Lavender Genotypes: A Synergistic Approach Integrating Machine Learning Techniques |
| title_sort | optimizing micropropagation and rooting protocols for diverse lavender genotypes a synergistic approach integrating machine learning techniques |
| topic | micropropagation rooting efficiency activated carbon BAP IBA |
| url | https://www.mdpi.com/2311-7524/10/1/52 |
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