A generalized regression neural network is another type of ANNs that has successfully been used for modeling and predicting different tissue culture processes . Although there exist no reports using GRNN for the modeling and optimization of disinfection process, we previously showed that GRNN has a higher predictive performance than RBF, MLP, and the adaptive neuro-fuzzy inference system for cannabis micropropagation . Therefore, in the current study, we used GRNN-GA to model and optimize cannabis seed disinfection. Mature seed germination can sometimes be more difficult than immature seed germination due to the increase in the seed coat’s impermeability and the accumulation of inhibitors during seed maturation . Hence, dormancy breaking plays a critical role relating to the speed and frequency of seed germination due to morpho-physiological dormancy.
Although there are no reports on the effects of scarification on cannabis seed germination, the positive impact of dormancy breaking by scarification has previously been suggested in several plants, such as Limodorum , Salvia stenophylla , and legumes . Based on this evidence, studying the effect of scarification on cannabis seed germination can pave the way for devising an in vitro seed germination protocol with high speed and germination frequency. The current study uses GRNN-GA to model and optimize cannabis seed disinfection, and investigates the effect of scarification on seed germination. By combining these procedures, a superior in vitro cannabis seed germination protocol that limits contamination while allowing high germination rates in a short timeframe was established. In vitro seed germination of cannabis has great potential to improve the efficiency of elite cultivar selection, pheno-hunting, phenotyping, and to support various in vitro culture methods as initial explant materials .
In orthodox seeds, germination typically initiates with the passive uptake of water by the dry mature seed, and terminates with radicle protrusion through the seed envelope . Different abiotic factors affect seed germination,mainly through regulating the signaling and metabolism pathways of abscisic acid and gibberellic acid . Although cannabis seeds easily germinate within several days under greenhouse or fifield conditions, in vitro cannabis seed germination tends to be more difficult, with lower germination rates spread over a longer period of time. The cause of this difference is unknown, but is likely related to the disinfection protocol that may stress the developing embryo or potentially eliminate microbes that play a role in the germination process. As such, optimizing sterilization and scarification protocols can be considered the two most important procedures for successful in vitro seed germination . The surface sterilization of initial source material, including seeds, is a prerequisite for the success of the culture . Therefore, it is vital to optimize the sterilization protocol while allowing it to remain simple, cheap, environmentally friendly, and efficient .
Although various disinfectants and immersion times can be employed to sterilize the explants, each species and even explant type necessitates a particular sterilization protocol . The hybrid of machine learning—optimization algorithm procedures offer promising computational methodology that is well suited to model and optimize in vitro culture systems such as sterilization . Based on our results, GRNN-GA accurately predicted and optimized the in vitro surface sterilization of cannabis seed. According to the optimization process through GRNN-GA, 4.6% sodium hypochlorite along with 0.008% hydrogen peroxide for 16.81 min would result in no contamination. Similar to our results, previous studies showed that sodium hypochlorite was more successful than hydrogen peroxide in controlling contamination . Additionally, the results of the validation experiment confirmed no differences between the optimized predicted and observed results, showing the robustness of GRNN-GA.
In line with our results, previous studies showed that GRNN-GA can be considered a reliable computational method with high prediction performance for the modeling and optimizing of in vitro culture systems . While the optimized seed disinfection protocol resulted in 0% contamination, germination was still slow and sporadic. The second experiment was performed to evaluate the effect of scarification on the speed and frequency of in vitro seed germination. One possible explanation for this difference is the presence of different microbes that aid in the digestion of the seed coat or micropyle plug, thereby facilitating higher rates of imbibition, and thus, higher/quicker field germination rates.