Machine learning (ML) algorithms and artificial neural network for optimizing in vitro germination and growth indices of industrial hemp (Cannabis sativa L.)

Aasım, Muhammad; Katırcı, Ramazan; Akgur, Ozlem; Yildirim, Büşra; Mustafa, Zemran; Nadeem, Muhammad Azhar; Baloch, Faheem Shahzad; Karaköy, Tolga; Yılmaz, Güngör

doi:10.1016/j.indcrop.2022.114801

Cited by 56 publications

(129 citation statements)

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“…RSM is a computer-based model, used for optimizing and predicting output variables using more than two input variables ( Abbasi et al, 2016 ; Managamuri et al, 2019 ; Askari et al, 2021 ; Slimani et al, 2021 ). The advantage of using contour plots is the distribution of attained results into different subunits, which enables to specify the input variables for the desired output variable ( Aasim et al, 2022 ). RSM predicted the optimal pretreatment and post-treatment BAP concentrations for inducing maximum shoot regeneration frequency, shoot counts and shoot length by estimating the R 2 (measured), R 2 (Adj.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, ML and ANN models have been successfully employed in plant tissue culture studies for optimizing different input variables like a basal medium ( Alanagh et al, 2014 ; Arab et al, 2016 ; Arab et al, 2018 ), PGR types, concentration for in vitro regeneration ( Kirtis et al, 2022 ), somatic embryogenesis ( Niazian et al, 2017 ), callogenesis ( Niazian et al, 2018 ), in vitro sterilization ( Hesami et al, 2019 ; Aasim et al, 2022 ), and in vitro induced double haploid production ( Niazian and Shariatpanahi, 2020 ). The detailed investigation of these studies revealed the use of different performance metrics like R 2 , MSE, RMSE, MAE, etc.…”

Section: Discussionmentioning

confidence: 99%

“…These types of issues can be overcome by modern high throughput technologies like machine learning (ML) and artificial neural network (ANN) models for testing and optimizing the output variables concerning the input parameters. Although the application of ML and ANN models in plant sciences specifically in the area of plant tissue culture is in its early stages, it is successfully documented for different aspects of plant tissue culture ranging from in vitro sterilization to in vitro regeneration and from in vitro callogenesis to secondary metabolite production ( Hesami et al, 2020a ; García-Pérez et al, 2020 ; Hameg et al, 2020 ; Hesami & Jones, 2020 ; Niazian and Niedbala, 2020 ; Pepe et al, 2021 ; Salehi et al, 2021 ; Aasim et al, 2022 ). In these studies, researchers employed different ML algorithms, and the selection of specific ML models is generally based on the expertise and target set in the study.…”

Section: Introductionmentioning

confidence: 99%

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Innovation in the Breeding of Common Bean Through a Combined Approach of in vitro Regeneration and Machine Learning Algorithms

et al. 2022

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Common bean is considered a recalcitrant crop for in vitro regeneration and needs a repeatable and efficient in vitro regeneration protocol for its improvement through biotechnological approaches. In this study, the establishment of efficient and reproducible in vitro regeneration followed by predicting and optimizing through machine learning (ML) models, such as artificial neural network algorithms, was performed. Mature embryos of common bean were pretreated with 5, 10, and 20 mg/L benzylaminopurine (BAP) for 20 days followed by isolation of plumular apice for in vitro regeneration and cultured on a post-treatment medium containing 0.25, 0.50, 1.0, and 1.50 mg/L BAP for 8 weeks. Plumular apice explants pretreated with 20 mg/L BAP exerted a negative impact and resulted in minimum shoot regeneration frequency and shoot count, but produced longer shoots. All output variables (shoot regeneration frequency, shoot counts, and shoot length) increased significantly with the enhancement of BAP concentration in the post-treatment medium. Interaction of the pretreatment × post-treatment medium revealed the need for a specific combination for inducing a high shoot regeneration frequency. Higher shoot count and shoot length were achieved from the interaction of 5 mg/L BAP × 1.00 mg/L BAP followed by 10 mg/L BAP × 1.50 mg/L BAP and 20 mg/L BAP × 1.50 mg/L BAP. The evaluation of data through ML models revealed that R2 values ranged from 0.32 to 0.58 (regeneration), 0.01 to 0.22 (shoot counts), and 0.18 to 0.48 (shoot length). On the other hand, the mean squared error values ranged from 0.0596 to 0.0965 for shoot regeneration, 0.0327 to 0.0412 for shoot count, and 0.0258 to 0.0404 for shoot length from all ML models. Among the utilized models, the multilayer perceptron model provided a better prediction and optimization for all output variables, compared to other models. The achieved results can be employed for the prediction and optimization of plant tissue culture protocols used for biotechnological approaches in a breeding program of common beans.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Innovation in the Breeding of Common Bean Through a Combined Approach of in vitro Regeneration and Machine Learning Algorithms

et al. 2022

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“…The reliability and applicability of machine learning as one of the powerful computational approaches have been recently reviewed in different areas of plant science such as in vitro culture [4], plant breeding [27], stress phenotyping [28], and system biology [29]. Moreover, the accuracy of ANNs has been recently approved for modeling, prediction, and optimization of different in vitro culture systems such as sterilization [20,30], seed germination [5,31], callogenesis [32,33], shoot proliferation [19,[34][35][36], somatic embryogenesis [37,38], androgenesis [39], gene transformation [40,41], and secondary metabolite production [42,43]. There are many approaches for optimizing the culture medium for plant tissue culture, but there is not a universal protocol that can be used to modify a micropropagation medium for a large number of plants.…”

Section: Discussionmentioning

confidence: 99%

Mathematical modeling and optimizing the in vitro shoot proliferation of wallflower using multilayer perceptron non-dominated sorting genetic algorithm-II (MLP-NSGAII)

2022

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Novel computational methods such as artificial neural networks (ANNs) can facilitate modeling and predicting results of tissue culture experiments and thereby decrease the number of experimental treatments and combinations. The objective of the current study is modeling and predicting in vitro shoot proliferation of Erysimum cheiri (L.) Crantz, which is an important bedding flower and medicinal plant. Its micropropagation has not been investigated before and as a case study multilayer perceptron- non-dominated sorting genetic algorithm-II (MLP-NSGAII) can be applied. MLP was used for modeling three outputs including shoots number (SN), shoots length (SL), and callus weight (CW) based on four variables including 6-benzylaminopurine (BAP), kinetin (Kin), 1-naphthalene acetic acid (NAA) and gibberellic acid (GA3). The R2 correlation values of 0.84, 0.99 and 0.93 between experimental and predicted data were obtained for SN, SL, and CW, respectively. These results proved the high accuracy of MLP model. Afterwards the model connected to Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was used to optimize input variables for obtaining the best predicted outputs. The results of sensitivity analysis indicated that SN and CW were more sensitive to BA, followed by Kin, NAA and GA. For SL, more sensitivity was obtained for GA3 than NAA. The validation experiment indicated that the difference between the validation data and MLP-NSGAII predicted data were negligible. Generally, MLP-NSGAII can be considered as a powerful method for modeling and optimizing in vitro studies.

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“…Modern ML methods come with a powerful set of algorithms that can self-learn, analyze complexities in datasets and predict, classify, estimate, simulate, the underlined trends and behaviors. Therefore, these methods provide better understanding of tissue culture processes and help us make correct decisions for optimization at every step [ 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of In vitro organogenesis of Bacopa monnieri using artificial neural networks and regression models

Viswanathan¹,

Gosukonda²,

Sherman³

et al. 2022

Heliyon

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Machine learning (ML) algorithms and artificial neural network for optimizing in vitro germination and growth indices of industrial hemp (Cannabis sativa L.)

Cited by 56 publications

References 61 publications

Innovation in the Breeding of Common Bean Through a Combined Approach of in vitro Regeneration and Machine Learning Algorithms

Innovation in the Breeding of Common Bean Through a Combined Approach of in vitro Regeneration and Machine Learning Algorithms

Mathematical modeling and optimizing the in vitro shoot proliferation of wallflower using multilayer perceptron non-dominated sorting genetic algorithm-II (MLP-NSGAII)

Prediction of In vitro organogenesis of Bacopa monnieri using artificial neural networks and regression models

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