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

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 (R 2 > 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.

Keywords: generalized regression neural network; genetic algorithm; hydrogen peroxide; plant tissue culture; scarification; seed dormancy; sodium hypochlorite.

Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Seed germination response of a noxious agricultural weed Echium plantagineum to temperature, light, pH, drought stress, salinity, heat and smoke

A Centre for Environmental Management, Faculty of Science and Technology, Federation University Australia, PO Box 663, Mount Helen, Vic. 3350, Australia.

B Centre for Plant Science, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Gatton, Qld 4343, Australia.

Crop and Pasture Science 69(3) 326-333 https://doi.org/10.1071/CP17308
Submitted: 25 August 2017 Accepted: 2 December 2017 Published: 19 February 2018

Abstract

Echium plantagineum is a significant pasture weed in the Mediterranean climatic zone of several countries, including Australia. This invasive weed, introduced as an ornamental into Australia (where it is known as Paterson’s curse), quickly became established and is now a significant weed of agriculture. Although E. plantagineum is a well-established, highly competitive weed that thrives under disturbance and is tolerant of a wide variety of conditions, including varying soil moisture and drought, and some aspects of its ecology remain unknown. This study investigated germination response to temperature and light, pH, soil moisture, salinity, and pre-germination exposure of seed to heat and smoke. Temperature was found to be more influential on germination than light and the species is tolerant to a wide range of pH. However, available moisture may limit germination, as may elevated salinity. Management of this weed requires approaches that minimise soil seedbank input or prevent germination of soil seedbanks.

Additional keywords: invasive, osmotic stress, Paterson’s curse, seed ecology.

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