Modeling and optimizing callus growth and development in Cannabis sativa using random forest and support vector machine in combination with a genetic algorithm

Hesami, Mohsen; Jones, Andrew Maxwell Phineas

doi:10.1007/s00253-021-11375-y

Cited by 47 publications

(32 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An alternative to address the inherent complexity of plant tissue culture systems is to apply machine learning methodology. This approach leverages modern computing power and developments in artificial intelligence to efficiently recognize patterns in complex and disorderly datasets, typical of what is observed in plant tissue culture ( Hesami and Jones, 2020 ; Hesami et al, 2021b ). Machine learning algorithms can then be combined with optimization algorithms to decipher complex interactions and predict theoretically optimized combinations of factors for desired outcomes.…”

Section: Discussionmentioning

confidence: 99%

“…Historically, micropropagation systems have been developed through serial manipulation and optimization of single factors, individually. Conventional statistical methods such as simple regression and ANOVA have typically been recommended for small databases with limited dimensions, and are therefore inappropriate for analyzing data derived from complex and non-linear processes such as light quality ( Hesami et al, 2021b ; Yoosefzadeh-Najafabadi et al, 2021a ). The high probability of overfitting is one of the main disadvantages of using conventional statistical methods ( Jafari and Shahsavar, 2020 ; Yoosefzadeh-Najafabadi et al, 2021b ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, artificial intelligence (AI) models combined with optimization algorithms (OAs) such as a genetic algorithm (GA) can be employed as an efficient and reliable computational method to inter-pret, forecast, and optimize this complex system [10][11][12][13][14]. This strategy (AI-OA) has been successfully used for modeling and optimizing different tissue culture systems, including in vitro decontamination, shoot proliferation, androgenesis, somatic embryogenesis, secondary metabolite production, and rhizogenesis [7,15,16]. Ivashchuk et al [17] employed multilayer perceptron (MLP) and radial basis function (RBF) as two well-known artificial neural networks (ANNs) for modeling and predicting the effect of different disinfectants and immersion times for Bellevalia sarmatica, Echinacea purpurea, and Nigella damascene explant decontamination.…”

Section: Introductionmentioning

confidence: 99%

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…In an effort to optimise in vitro conditions for the production of undifferentiated callus and somatic embryogenic callus, the study by Hesami et al [92] utilised a computergenerated machine learning algorithm as a visualisation tool. Various concentrations of 2,4-D and kinetin were tested and the embryogenic tissues were best generated with 0.5 mg/L 2,4-D and 0.25 mg/L kinetin or a combination of 1 mg/L 2,4-D and 0.5 mg/L kinetin, albeit at low rates (10 or 20%, respectively).…”

Section: Synthetic Seed Technologymentioning

confidence: 99%

Cannabis sativa: From Therapeutic Uses to Micropropagation and Beyond

Adams

Malatsi

Makunga

2021

Plants

View full text Add to dashboard Cite

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.

show abstract

Modeling and optimizing callus growth and development in Cannabis sativa using random forest and support vector machine in combination with a genetic algorithm

Cited by 47 publications

References 43 publications

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

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

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

Cannabis sativa: From Therapeutic Uses to Micropropagation and Beyond

Contact Info

Product

Resources

About