Modeling and optimizing in vitro seed germination of industrial hemp (Cannabis sativa L.)

Hesami, Mohsen; Pepe, Marco; Monthony, Adrian S.; Baiton, Austin; Jones, Andrew Maxwell Phineas

doi:10.1016/j.indcrop.2021.113753

Cited by 52 publications

(42 citation statements)

References 57 publications

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“…We previously reported the effect of different types and strengths of media in addition to carbohydrate types and levels as primarily important factors contributing to in vitro cannabis seed germination indices and morphological seedling traits [5]. Our results demonstrated that maximum germination percentage (82.67 ± 3.837%) was achieved with 0.43 strength mMS medium and 2.3% sucrose [5].…”

Section: Introductionmentioning

confidence: 56%

“…An efficient in vitro seed germination system would also support downstream biotechnologies (regeneration, transformation, etc.) in which seedling-derived tissues are preferred [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…It was reported that MLP-NSGA-II had a high performance to predict and optimize the system [13]. A generalized regression neural network (GRNN) is another type of ANNs that has successfully been used for modeling and predicting different tissue culture processes [5,[18][19][20]. 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 (ANFIS) for cannabis micropropagation [5,18].…”

Section: Introductionmentioning

confidence: 99%

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

Pepe

Hesami

Jones

2021

Plants

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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.

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Section: Introductionmentioning

confidence: 56%

“…An efficient in vitro seed germination system would also support downstream biotechnologies (regeneration, transformation, etc.) in which seedling-derived tissues are preferred [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Pepe

Hesami

Jones

2021

Plants

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“…In recent years, machine learning algorithms such as artificial neural networks (ANNs) and neuro-fuzzy logic have been successfully applied for modeling and predicting various in vitro culture systems such as shoot growth and development, callogenesis, somatic embryogenesis, androgenesis, secondary metabolite production, and rhizogenesis ( Hesami and Jones, 2020 ; Niazian and Niedbała, 2020 ). However, in most plant tissue culture studies, individual models were employed, and the efficiency of different machine learning algorithms has not been compared ( Hesami et al, 2021c ).…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Machine Learning and Evolutionary Optimization Algorithms for Precision Micropropagation of Cannabis sativa: Prediction and Validation of in vitro Shoot Growth and Development Based on the Optimization of Light and Carbohydrate Sources

Pepe

Hesami

Small³

et al. 2021

Front. Plant Sci.

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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 analyzing 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 μmol/m2/s in combination with sucrose concentrations ranging from 1 to 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 (GA), biogeography-based optimization (BBO), interior search algorithm (ISA), and symbiotic organisms search (SOS)] 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 favorable 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.

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“…Therefore, the use of machine learning may provide more efficient combinations that could assist with high recovery of embryo-like structures from callus. With the intention to refine the medium components and carbohydrate resources in DKW and MS media, Hesami et al [93] employed machine learning algorithms to test predicted concentrations of glucose and sucrose for their efficiency in promoting seed germination and seedling development using an in vitro-based assay. These authors tested the accuracy of the concentrations generated by predictive models.…”

Section: Synthetic Seed Technologymentioning

confidence: 99%

Cannabis sativa: From Therapeutic Uses to Micropropagation and Beyond

Adams

Malatsi

Makunga

2021

Plants

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The development of a protocol for the large-scale production of Cannabis and its variants with little to no somaclonal variation or disease for pharmaceutical and for other industrial use has been an emerging area of research. A limited number of protocols have been developed around the world, obtained through a detailed literature search using web-based database searches, e.g., Scopus, Web of Science (WoS) and Google Scholar. This article reviews the advances made in relation to Cannabis tissue culture and micropropagation, such as explant choice and decontamination of explants, direct and indirect organogenesis, rooting, acclimatisation and a few aspects of genetic engineering. Since Cannabis micropropagation systems are fairly new fields, combinations of plant growth regulator experiments are needed to gain insight into the development of direct and indirect organogenesis protocols that are able to undergo the acclimation stage and maintain healthy plants desirable to the Cannabis industry. A post-culture analysis of Cannabis phytochemistry after the acclimatisation stage is lacking in a majority of the reviewed studies, and for in vitro propagation protocols to be accepted by the pharmaceutical industries, phytochemical and possibly pharmacological research need to be undertaken in order to ascertain the integrity of the generated plant material. It is rather difficult to obtain industrially acceptable micropropagation regimes as recalcitrance to the regeneration of in vitro cultured plants remains a major concern and this impedes progress in the application of genetic modification technologies and gene editing tools to be used routinely for the improvement of Cannabis genotypes that are used in various industries globally. In the future, with more reliable plant tissue culture-based propagation that generates true-to-type plants that have known genetic and metabolomic integrity, the use of genetic engineering systems including “omics” technologies such as next-generation sequencing and fast-evolving gene editing tools could be implemented to speed up the identification of novel genes and mechanisms involved in the biosynthesis of Cannabis phytochemicals for large-scale production.

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Modeling and optimizing in vitro seed germination of industrial hemp (Cannabis sativa L.)

Cited by 52 publications

References 57 publications

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

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

Comparative Analysis of Machine Learning and Evolutionary Optimization Algorithms for Precision Micropropagation of Cannabis sativa: Prediction and Validation of in vitro Shoot Growth and Development Based on the Optimization of Light and Carbohydrate Sources

Cannabis sativa: From Therapeutic Uses to Micropropagation and Beyond

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