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Maximum rooting observed in the shoots inoculated on MS medium supplemented with 4 μM NAA and 0.07% charcoal
Source publication
In this paper, we compared the efficacy of observation based modeling approach using a genetic algorithm with the regular statistical analysis as an alternative methodology in plant research. Preliminary experimental data on in vitro rooting was taken for this study with an aim to understand the effect of charcoal and naphthalene acetic acid (NAA)...
Citations
... In contrast, common machine learning approaches, including neural networks and support vector machines, although powerful in fields like computer vision and natural language processing, are typically non-transparent and less interpretable due to their black-box nature [9]. Symbolic regression offers a solution, searching for mathematical relationships between variables without predefined assumptions, distinguishing it from conventional regression, which relies on predefined functions [10]. It explores a broad range of mathematical operations to uncover potentially complex and non-linear relationships which traditional methods may not reveal. ...
... To tackle this impediment, machine learning algorithms can be used as an alternative mathematical method [8][9][10][11]. Different machine learning algorithms (e.g., artificial neural networks (ANNs), neuro-fuzzy logic systems, support vector machine (SVM), and random forest) have been recently used for modeling and predicting various in vitro culture systems such as explant sterilization [12,13], in vitro seed germination [14], callogenesis [15][16][17], androgenesis [18], shoot proliferation [19,20], rhizogenesis [21,22], in vitro secondary metabolite production [8,23,24], and gene transformation [25,26]. Among machine learning algorithms, different types of ANNs such as multilayer perceptron (MLP), radial basis function (RBF), and generalized regression neural network (GRNN) have been widely employed to model and predict in vitro culture processes [27]. ...
... The somatic embryogenesis in carrot has been previously studied [21,29,30,[32][33][34][35]. Establishing carrot somatic embryogenesis was associated with different obstacles such as low efficiency of somatic embryogenesis, chimeric callogenesis consisting of both non-embryogenic and embryogenic calli [21,29,30,[32][33][34][35]. ...
... The somatic embryogenesis in carrot has been previously studied [21,29,30,[32][33][34][35]. Establishing carrot somatic embryogenesis was associated with different obstacles such as low efficiency of somatic embryogenesis, chimeric callogenesis consisting of both non-embryogenic and embryogenic calli [21,29,30,[32][33][34][35]. Data mining approaches may help in reducing trial and errors in the process of optimizing carrot somatic embryogenesis. ...
Introduction
Developing somatic embryogenesis is one of the main steps in successful in vitro propagation and gene transformation in the carrot. However, somatic embryogenesis is influenced by different intrinsic (genetics, genotype, and explant) and extrinsic (e.g., plant growth regulators (PGRs), medium composition, and gelling agent) factors which cause challenges in developing the somatic embryogenesis protocol. Therefore, optimizing somatic embryogenesis is a tedious, time-consuming, and costly process. Novel data mining approaches through a hybrid of artificial neural networks (ANNs) and optimization algorithms can facilitate modeling and optimizing in vitro culture processes and thereby reduce large experimental treatments and combinations. Carrot is a model plant in genetic engineering works and recombinant drugs, and therefore it is an important plant in research works. Also, in this research, for the first time, embryogenesis in carrot (Daucus carota L.) using Genetic algorithm (GA) and data mining technology has been reviewed and analyzed.
Materials and methods
In the current study, data mining approach through multilayer perceptron (MLP) and radial basis function (RBF) as two well-known ANNs were employed to model and predict embryogenic callus production in carrot based on eight input variables including carrot cultivars, agar, magnesium sulfate (MgSO4), calcium dichloride (CaCl2), manganese (II) sulfate (MnSO4), 2,4-dichlorophenoxyacetic acid (2,4-D), 6-benzylaminopurine (BAP), and kinetin (KIN). To confirm the reliability and accuracy of the developed model, the result obtained from RBF-GA model were tested in the laboratory.
Results
The results showed that RBF had better prediction efficiency than MLP. Then, the developed model was linked to a genetic algorithm (GA) to optimize the system. To confirm the reliability and accuracy of the developed model, the result of RBF-GA was experimentally tested in the lab as a validation experiment. The result showed that there was no significant difference between the predicted optimized result and the experimental result.
Conclutions
Generally, the results of this study suggest that data mining through RBF-GA can be considered as a robust approach, besides experimental methods, to model and optimize in vitro culture systems. According to the RBF-GA result, the highest somatic embryogenesis rate (62.5%) can be obtained from Nantes improved cultivar cultured on medium containing 195.23 mg/l MgSO4, 330.07 mg/l CaCl2, 18.3 mg/l MnSO4, 0.46 mg/l 2,4- D, 0.03 mg/l BAP, and 0.88 mg/l KIN. These results were also confirmed in the laboratory
... Recently plant tissue culture is recognising the benefits of the application of artificial intelligence and optimization algorithms in spite of their complexity in genetic programming and modelling [5,17]. Radial Basis Function (RBF), Generalized Regression Neural Network (GRNN), Probabilistic Neural Network (PNN), Neuro-Fuzzy Logic (NFL), Support Vector Machine (SVM), and Multilayer Perceptron (MLP) are some of the most widely used artificial neural networks (ANNs) for modeling and protocol optimization in plant tissue culture. ...
In vitro, sterilization is one of the key components for proceeding with plant tissue cultures. Since the effectiveness of sterilization has a direct impact on the culture's final outcomes, there is a crucial need for optimization of the sterilization process. However, compared with traditional optimizing methods, the use of computational approaches through artificial intelligence-based process modeling and optimization algorithms provides a precise optimal condition for in vitro culturing. This study aimed to optimise in vitro sterilization of grape rootstock 3309C using RSM, ANN, and genetic algorithm (GA) techniques. In this context, two output responses, namely, Clean Culture and Explant Viability, were optimised using the models developed by RSM and ANN, followed by a GA, to obtain a globally optimal solution. The most influential independent factors, such as HgCl2, NaOCl, AgNO3, and immersion time, were considered input variables. The significance of the developed models was investigated with statistical and non-statistical techniques and was optimised to determine the significance of selected inputs. The optimal clean culture of 91%, and the explant viability of 89% can be obtained from 1.62% NaOCl at a 13.96 min immersion time, according to MLP-NSGAII. Sensitivity analysis revealed that the clean culture and explant viability were less sensitive to AgNO3 and more sensitive to immersion time. Results showed that the differences between the GA predicted and validation data were significant after the performance validation of predicted and optimised sterilising agents with immersion time combinations were tested. In general, GA, a potent methodology, may open the door to the development of new computational methods in plant tissue culture.
... This method relies on genetic algorithms and is especially suitable for plant tissue culture. The whole process takes into account the different culture stages of different plant tissues (such as embryo [75], callus [78], bud [79], and root [80]). The modeling, prediction, and optimization of plant genotypes, media, sterilization conditions, different types, and concentrations of plant growth regulators also need to be considered [75]. ...
... As shown in Figure 3, data-driven models are effectively used for different purposes in plant tissue culture. AI and OA have been effectively applied to predict and optimize the length and number of micro-buds [79,81] or roots [80], plant cell culture or hairy root biomass [82], and culture environmental conditions (such as temperature and sterilization [83,84]) to achieve the maximum productivity and efficiency as well as the classification of micro-buds and somatic embryos. Future AI-OA methods could also be used in the development of genetic engineering and genome editing. ...
At present, most precious compounds are still obtained by plant cultivation such as ginsenosides, glycyrrhizic acid, and paclitaxel, which cannot be easily obtained by artificial synthesis. Plant tissue culture technology is the most commonly used biotechnology tool, which can be used for a variety of studies such as the production of natural compounds, functional gene research, plant micropropagation, plant breeding, and crop improvement. Tissue culture material is a basic and important part of this issue. The formation of different plant tissues and natural products is affected by growth conditions and endogenous substances. The accumulation of secondary metabolites are affected by plant tissue type, culture method, and environmental stress. Multi-domain technologies are developing rapidly, and they have made outstanding contributions to the application of plant tissue culture. The modes of action have their own characteristics, covering the whole process of plant tissue from the induction, culture, and production of natural secondary metabolites. This paper reviews the induction mechanism of different plant tissues and the application of multi-domain technologies such as artificial intelligence, biosensors, bioreactors, multi-omics monitoring, and nanomaterials in plant tissue culture and the production of secondary metabolites. This will help to improve the tissue culture technology of medicinal plants and increase the availability and the yield of natural metabolites.
... Symbolic regression has been identified as a promising solution for obtaining explanatory models without imposing any a priori assumptions [57]. Symbolic regression has seen development using both genetic programming and simulated annealing; however, as shown in [58], symbolic regression using simulated annealing generally outperforms symbolic regression based on genetic programming. ...
Process cooling for food production is an energy-intensive industry with complex interactions and restrictions that complicate the ability to utilize energy-flexibility due to unforeseen consequences in production. Therefore, methods for assessing the potential flexibility in individual facilities to enable the active participation of process-cooling facilities in the electricity system are essential, but not yet well discussed in the literature. Therefore, this paper introduces an assessment method based on multi-method simulation and multi-objective optimization for investigating energy flexibility in process cooling, with a case study of a Danish process-cooling facility for canned-meat food production. Multi-method simulation is used in this paper: multi-agent-based simulation to investigate individual entities within the process-cooling system and the system’s behavior; discrete-event simulation to explore the entire process-cooling flow; and system dynamics to capture the thermophysical properties of the refrigeration unit and states of the refrigerated environment. A simulation library is developed, and is able to represent a generic production-flow of the canned-food process cooling. A data-driven symbolic-regression approach determines the complex logic of individual agents. Using a binary tuple-matrix for refrigeration-schedule optimization, the refrigeration-cycle operation is determined, based on weather forecasts, electricity price, and electricity CO2 emissions without violating individual room-temperature limits. The simulation results of one-week’s production in October 2020 show that 32% of energy costs can be saved and 822 kg of CO2 emissions can be reduced. The results thereby show the energy-flexibility potential in the process-cooling facilities, with the benefit of overall production cost and CO2 emissions reduction; at the same time, the production quality and throughput are not influenced.
... Different machine learning algorithms (e.g., ANNs, neuro-fuzzy logic systems, support vector machine (SVM), and random forest) have been recently used for modeling and predicting various in vitro culture systems such as explant sterilization [13,20], in vitro seed germination [21], callogenesis [22][23][24], androgenesis [25], shoot proliferation [15,26], rhizogenesis [27], in vitro secondary metabolite production [28][29][30], and gene transformation [31,32]. Among machine learning algorithms, different types of ANNs, such as MLP, radial basis function (RBF), and generalized regression neural network (GRNN), have been widely employed to model and predict in vitro culture processes [16,33,34]. ...
Background
Callus induction is the first step in optimizing plant regeneration. Fit embryogenesis and shooting rely on callus induction. In addition, using artificial intelligence models in combination with an algorithm can be helpful in the optimization of in vitro culture. The present study aimed to evaluate the percentage and speed of callus induction optimization in carrot with a Multilayer Perceptron-Single point discrete genetic algorithm (GA).
Materials and methods
In this study, the outputs included callus induction percentage and speed, while inputs were different types and concentrations of plant growth regulator (0. 5, 0.2 mg/l 2,4-D, 0.3, 0.2, 0.5 mg/l BAP, 1, 0.2 mg/l Kin, and 2 mg/l NAA), different explants (shoot, root, leaf, and nodal), a different concentration compound of MS medium (1 × MS, 4× MS, and 8× MS) and time of sampling. The data were obtained in the laboratory, and multilayer perceptron (MLP) and radial basis function (RBF), two well-known ANNs, were employed to model. Then, GA was used for optimization, and sensitivity analysis was performed to indicate the inputs’ importance.
Results
The results showed that MLP had better prediction efficiency than RBF. Based on the results, R ² in training and testing data was 95 and 95% for the percentage of callus induction, while it was 94 and 95% for the speed of callus induction, respectively. In addition, a concentration compound of MS had high sensitivity, while times of sampling had low sensitivity. Based on the MLP-Single point discrete GA, the best results were obtained for shoot explants, 1× MS media, and 0.5 mg/l 2, 4-D + 0.5 mg/l BAP. Further, a non-significant difference was observed between the test result and predicted MLP.
Conclusions
Generally, MLP-Single point discrete GA is considered a potent tool for predicting treatment and fit model results used in plant tissue culture and selecting the best medium for callus induction.
... 99 % of shoots initiated roots within 21 days of shoot inoculation. Mridula, Nair and Kumar13 reported five roots per shoot explant of WTR inoculated on MS medium which is augmented with NAA (0.07% charcoal and 4µM). Similarly, John et al 10 reported 4.7 roots per nodal shoot explants of WTR on MS media amended with IBA (0.8 mg/L).On the other hand, Purohit and Kukda 14,15 observed 2.8 roots per nodal shoot explants of WTR with maximum root length of 5.4 cm on MS media amended with IBA (100 mg/L), 4.6 roots per nodal shoot explants of WTR with maximum root length of 5.6 cm on MS media amended with IBA (500 mg/L) respectively. ...
Wrightia tinctoria Roxb. (WTR) is a medicinal plant with soft wood that belongs to Apocynaceae family. The therapeutic constituents of this plant are used in treating many human ailments like psoriasis, diabetics and cancer. In view of low percent of natural regeneration, over exploitation due to its medicinal (treatment of human ailments) and commercial importance (toy making and pala indigo dye) and the hurdles faced by the Etikoppaka toy making industry (Vizag, A.P. India), there is dier need for conservation and production of propagules of this valuable soft woody and medicinally important tree. This study is undertaken to optimize and develop large scale regeneration and hardening protocol in Vadlamudi, Guntur Dt., A.P. using nodal explants of fifteen days old (in vitro) seedling. The nodal explants are inoculated on MS media amended with BAP (1.5 mg/L) + NAA (0.1 mg/L), induced 14 shoots per explant in 30 days with a length of 5.2 cm and 85-90% shoot initiation. The shoots when inoculated on Murashige Skoog media reinforce with IBA (3.5mg/L), showed eight roots per one shoot explant with maximum length of 6.9 cm. 99 % shoots initiated roots within 21 days of shoot inoculation. After 28 days of rooting, the plantlets are acclimatized to natural conditions through different stages and the % of survival rates of plantlets in the natural conditions was found to be 55 %.
... A detailed explanation of these input factors, their sources, and expected effects on species richness is given in Table 2 The sensitivity of alpha diversity as an expression of the previously described habitat conditions was analyzed using Eureqa software (Dubčáková 2011). The sensitivity of the independent variable was calculated based on the partial derivative method (Mridula et al. 2018). In addition, the categorical factor was tested to evaluate alpha diversity differences. ...
Climate and land-use change profoundly affect plant species distribution (SD) and composition, and the impact of these processes is expected to increase in the coming years. As a proxy of global changes, knowledge of SD and diversity along climatic gradients is essential to determine the efforts needed for species conservation. Plant spectral diversity is an emerging approach used as a proxy for species diversity based on remote sensing. Thus, the research aim was to develop a comprehensive methodology based on spectral diversity for SD and richness mapping and to study their relations with environmental and human-derived factors, demonstrated along Mediterranean to semi-arid climatic gradient. The study addresses two main knowledge gaps regarding spectral diversity: (1) improving the accuracy of woody species classification by features extraction and selection, and by using texture analysis in an ecosystem characterized by high spatial variability and relatively small-sized and sparse woody vegetation; and (2) developing a better estimate of the local species richness and their response to environmental and human-derived factors (i.e. climate, topography, substrate, and land cover factors) across a transition zone between Mediterranean woodlands and semi-arid dwarf shrublands. A hyperspectral image was acquired for a 43-km strip along the study area using an airborne flight of AISA-FENIX (380–2500 nm, 420 bands) at the end of the 2017 rainy season. The dominant species were surveyed, with a total number of 247 trees and shrubs, to train a machine learning support vector machine (SVM) classification for species distribution mapping, which yielded an overall accuracy of 86.1%. A feature extraction and selection methodology was developed, combining principal component analysis and neighborhood component analysis techniques, facilitating the identification of 33 spectral diagnostic bands out of 330 spectral bands. The classification accuracy was decreased by about 2% to 84.2% using only 33 spectral bands. The classification accuracy improved by about 7.1% for the seven large crown species (93.3%) by adding texture information. Later, the local species richness was calculated by utilizing the alpha diversity index (i.e. the Shannon Index) for 30-m grid cells and was tested in response to environmental (i.e. climate, substrate, and topography) and human-derived factors (i.e. land cover). The highest sensitivity to alpha diversity factors was mean annual precipitation, slope, and land surface temperature. The alpha diversity showed higher richness in the natural Mediterranean shrubland and the guarrigue located in the northern part of the climate gradient. We suggest that the approach presented here significantly improves the estimation of woody species distribution and diversity in areas characterized by high spatial heterogeneity along steep climatic gradients.
... Successful in vitro rooting and acclimatization as ultimate stages are very important in plant tissue culture (Mridula et al. 2018;Shukla et al. 2020). Both steps strongly depend on different factors such as auxin concentrations (Gago et al. 2010a;Niazian 2019). ...
Artificial intelligence (AI) models and optimization algorithms (OA) are broadly employed in different fields of technology and science and have recently been applied to improve different stages of plant tissue culture. The usefulness of the application of AI-OA has been demonstrated in the prediction and optimization of length and number of microshoots or roots, biomass in plant cell cultures or hairy root culture, and optimization of environmental conditions to achieve maximum productivity and efficiency, as well as classification of microshoots and somatic embryos. Despite its potential, the use of AI and OA in this field has been limited due to complex definition terms and computational algorithms. Therefore, a systematic review to unravel modeling and optimizing methods is important for plant researchers and has been acknowledged in this study. First, the main steps for AI-OA development (from data selection to evaluation of prediction and classification models), as well as several AI models such as artificial neural networks (ANNs), neurofuzzy logic, support vector machines (SVMs), decision trees, random forest (FR), and genetic algorithms (GA), have been represented. Then, the application of AI-OA models in different steps of plant tissue culture has been discussed and highlighted. This review also points out limitations in the application of AI-OA in different plant tissue culture processes and provides a new view for future study objectives.
Key points
• Artificial intelligence models and optimization algorithms can be considered a novel and reliable computational method in plant tissue culture.
• This review provides the main steps and concepts for model development.
• The application of machine learning algorithms in different steps of plant tissue culture has been discussed and highlighted.
... Therefore, it would be difficult to predict different in vitro culture parameters such as callogenesis rate, embryogenesis rate, and the number of somatic embryos as well as optimize factors involved in these parameters by simple conventional mathematical methods [22][23][24]. Furthermore, biological processes such as somatic embryogenesis cannot be described as a simple stepwise algorithm, especially when the datasets are highly noisy and complex [25][26][27][28][29]. Therefore, machine learning algorithms can be employed as an efficient and reliable computational methodology to interpret and predict different unpredictable datasets [30][31][32][33][34]. Recently, Multilayer Perceptron (MLP) as one of the common artificial neural networks (ANNs) has been widely employed for modeling and predicting in vitro culture systems such as in vitro sterilization [35,36], callogenesis [37][38][39], cell growth and protoplast culture [40,41], somatic embryogenesis [38,42,43], shoot regeneration [25,[44][45][46], androgenesis [47], hairy root culture [48,49], and in vitro rooting and acclimatization [31]. ...
Background
Optimizing the somatic embryogenesis protocol can be considered as the first and foremost step in successful gene transformation studies. However, it is usually difficult to achieve an optimized embryogenesis protocol due to the cost and time-consuming as well as the complexity of this process. Therefore, it is necessary to use a novel computational approach, such as machine learning algorithms for this aim. In the present study, two machine learning algorithms, including Multilayer Perceptron (MLP) as an artificial neural network (ANN) and support vector regression (SVR), were employed to model somatic embryogenesis of chrysanthemum, as a case study, and compare their prediction accuracy.
Results
The results showed that SVR (R2 > 0.92) had better performance accuracy than MLP (R2 > 0.82). Moreover, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was also applied for the optimization of the somatic embryogenesis and the results showed that the highest embryogenesis rate (99.09%) and the maximum number of somatic embryos per explant (56.24) can be obtained from a medium containing 9.10 μM 2,4-dichlorophenoxyacetic acid (2,4-D), 4.70 μM kinetin (KIN), and 18.73 μM sodium nitroprusside (SNP). According to our results, SVR-NSGA-II was able to optimize the chrysanthemum’s somatic embryogenesis accurately.
Conclusions
SVR-NSGA-II can be employed as a reliable and applicable computational methodology in future plant tissue culture studies.
