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Protocol

Machine Learning-Mediated Development and Optimization of Disinfection Protocol and Scarification Method for Improved In Vitro Germination of Cannabis Seeds

Department of Plant Agriculture, Gosling Research Institute for Plant Preservation, University of Guelph, Guelph, ON N1G 2W1, Canada
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Author to whom correspondence should be addressed.
Academic Editors: Milan S. Stankovic, Paula Baptista and Petronia Carillo
Plants 2021, 10(11), 2397; https://doi.org/10.3390/plants10112397
Received: 19 October 2021 / Revised: 1 November 2021 / Accepted: 5 November 2021 / Published: 6 November 2021
(This article belongs to the Special Issue 10th Anniversary of Plants—Recent Advances and Perspectives)
In vitro seed germination is a useful tool for developing a variety of biotechnologies, but cannabis has presented some challenges in uniformity and germination time, presumably due to the disinfection procedure. Disinfection and subsequent growth are influenced by many factors, such as media pH, temperature, as well as the types and levels of contaminants and disinfectants, which contribute independently and dynamically to system complexity and nonlinearity. Hence, artificial intelligence models are well suited to model and optimize this dynamic system. The current study was aimed to evaluate the effect of different types and concentrations of disinfectants (sodium hypochlorite, hydrogen peroxide) and immersion times on contamination frequency using the generalized regression neural network (GRNN), a powerful artificial neural network (ANN). The GRNN model had high prediction performance (R2 > 0.91) in both training and testing. Moreover, a genetic algorithm (GA) was subjected to the GRNN to find the optimal type and level of disinfectants and immersion time to determine the best methods for contamination reduction. According to the optimization process, 4.6% sodium hypochlorite along with 0.008% hydrogen peroxide for 16.81 min would result in the best outcomes. The results of a validation experiment demonstrated that this protocol resulted in 0% contamination as predicted, but germination rates were low and sporadic. However, using this sterilization protocol in combination with the scarification of in vitro cannabis seed (seed tip removal) resulted in 0% contamination and 100% seed germination within one week. View Full-Text
Keywords: hydrogen peroxide; sodium hypochlorite; generalized regression neural network; genetic algorithm; scarification; seed dormancy; plant tissue culture hydrogen peroxide; sodium hypochlorite; generalized regression neural network; genetic algorithm; scarification; seed dormancy; plant tissue culture
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MDPI and ACS Style

Pepe, M.; Hesami, M.; Jones, A.M.P. Machine Learning-Mediated Development and Optimization of Disinfection Protocol and Scarification Method for Improved In Vitro Germination of Cannabis Seeds. Plants 2021, 10, 2397. https://doi.org/10.3390/plants10112397

AMA Style

Pepe M, Hesami M, Jones AMP. Machine Learning-Mediated Development and Optimization of Disinfection Protocol and Scarification Method for Improved In Vitro Germination of Cannabis Seeds. Plants. 2021; 10(11):2397. https://doi.org/10.3390/plants10112397

Chicago/Turabian Style

Pepe, Marco, Mohsen Hesami, and Andrew Maxwell Phineas Jones. 2021. "Machine Learning-Mediated Development and Optimization of Disinfection Protocol and Scarification Method for Improved In Vitro Germination of Cannabis Seeds" Plants 10, no. 11: 2397. https://doi.org/10.3390/plants10112397

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