Comparative analysis of machine learning and evolutionary optimization algorithms for precision tissue culture ofCannabis sativa: Prediction and validation ofin vitroshoot growth and development based on the optimization of light and carbohydrate sources

Abstract

Micropropagation techniques offer opportunity to proliferate, maintain, and study dynamic plant responses in highly controlled environments without confounding external influences, forming the basis for many biotechnological applications. With medicinal and recreational interests for Cannabis sativa L. growing, research related to the optimization of in vitro practices is needed to improve current methods while boosting our understanding of the underlying physiological processes. Unfortunately, due to the exorbitantly large array of factors influencing tissue culture, existing approaches to optimize in vitro methods are tedious and time-consuming. Therefore, there is great potential to use new computational methodologies for analysing data to develop improved protocols more efficiently. Here, we first tested the effects of light qualities using assorted combinations of Red, Blue, Far Red, and White spanning 0-100 umol/m2/s in combination with sucrose concentrations ranging from 1-6 % (w/v), totaling 66 treatments, on in vitro shoot growth, root development, number of nodes , shoot emergence, and canopy surface area. Collected data were then assessed using multilayer perceptron (MLP), generalized regression neural network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS) to model and predict in vitro Cannabis growth and development. Based on the results, GRNN had better performance than MLP or ANFIS and was consequently selected to link different optimization algorithms (genetic algorithm, biogeography-based optimization, interior search algorithm, and symbiotic organisms search) for prediction of optimal light levels (quality/intensity) and sucrose concentration for various applications. Predictions of in vitro conditions to refine growth responses were subsequently tested in a validation experiment and data showed no significant differences between predicted optimized values and observed data. Thus, this study demonstrates the potential of machine learning and optimization algorithms to predict the most favourable light combinations and sucrose levels to elicit specific developmental responses. Based on these, recommendations of light and carbohydrate levels to promote specific developmental outcomes for in vitro Cannabis are suggested. Ultimately, this work showcases the importance of light quality and carbohydrate supply in directing plant development as well as the power of machine learning approaches to investigate complex interactions in plant tissue culture.

Cannabis sativa: From Therapeutic Uses to Micropropagation and Beyond

“…As a result of the significant economic implications of drug-type Cannabis for healthrelated industries, it is becoming more critical to develop ways to produce high-quality biomass with consistent secondary metabolite profiles and this particular goal can be achieved in part by micropropagation [35,43]. That being said, the market for legal hemp for CBD production and medical Cannabis is rapidly expanding and producers are turning to advanced scientific procedures as an option to lower the costs of production and offer Cannabis varieties that are healthy, scalable, and also of high quality [44].…”

“…However, because of the competitive advantage granted inside the industry, most advancements in this in vitro field are kept a 'trade secret' [44] and are thus not available in the public space as producers are always concerned about having a competitive edge. Cannabis micropropagation has been mostly an underground activity with few peer-reviewed studies [43]. This dearth of knowledge regarding in vitro Cannabis protocols has restricted the crop's biotechnological potential [84] as most species are cultivated in conditions optimised for other species with slight alterations, and are not fully optimised for any given use, due to cost and time constraints.…”

“…Despite the many challenges that come with in vitro micropropagation, some successful protocols with minimum risk of somaclonal variation in Cannabis have been implemented; however, an efficient and robust protocol is yet to be fully developed for many different varieties that are available to the general public, scientists and agricultural sector [43,54,63,70,74]. There is thus much knowledge in terms of agricultural practices and cultivation procedures in growing this species as a crop, within those that operate in the underground Cannabis business, that is not publicly available and much of this is associated with the long history of the plant being prohibited for public consumption and its illegal status in many governments throughout the world.…”