Abstract
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.
Highlights
Cannabis sativa L. has been widely used around the world for various applications [1]
There exist no reports using generalized regression neural network (GRNN) for the modeling and optimization of disinfection process, we previously showed that GRNN has a higher predictive performance than radial basis function (RBF), multilayer perceptron (MLP), and the adaptive neuro-fuzzy inference system (ANFIS) for cannabis micropropagation [5,18]
Performance indices (RMSE and MBE) of the developed GRNN demonstrated that the obtained result was highly precise, and correlated in both training and testing sets (Table 2)
Summary
Cannabis sativa L. has been widely used around the world for various applications (e.g., textiles, food, cosmetics) [1]. These days, interest has been focused on medicinal and recreational facets, furthering commercial expansion. Despite the reliance on clonal propagation, there is a continual need to germinate seeds to select new elite genotypes, perform pheno-hunting, as well as supporting breeding programs. To select new elite genotypes, plants are started from seed (technically achenes [3], but will be referred to as seed ). Once the elite genotypes are selected, the cutting is used as a source to propagate the clonal line. Maintaining the large population of cuttings during the phenotyping exercise represents a significant cost to producers and leaves the cutting derived mother plants exposed to insects and diseases
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