Analysis of macro nutrient related growth responses using multivariate adaptive regression splines

Akın, Melekşen; Eyduran, Sadiye Peral; Eyduran, Ecevit; Reed, Barbara M.

doi:10.1007/s11240-019-01763-8

Cited by 59 publications

(48 citation statements)

References 22 publications

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“…Variables can be clustered into two groups including quantitative (continuous and discrete) and qualitative (ordinal and nominal). Names with two or more classes without a hierarchical order are categorized as nominal variables, while ordinal data have distinct order (level X is more intense than level Y) [57,59]. Counts that include integers are classified as discrete data, while measurements along a continuum, which could be included smaller fractions are categorized as continuous variables [60].…”

Section: Plos Onementioning

confidence: 99%

The application of artificial neural networks in modeling and predicting the effects of melatonin on morphological responses of citrus to drought stress

Jafari

Shahsavar

2020

PLoS ONE

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Drought stress as one of the most devastating abiotic stresses affects agricultural and horticultural productivity in many parts of the world. The application of melatonin can be considered as a promising approach for alleviating the negative impact of drought stress. Modeling of morphological responses to drought stress can be helpful to predict the optimal condition for improving plant productivity. The objective of the current study is modeling and predicting morphological responses (leaf length, number of leaves/plants, crown diameter, plant height, and internode length) of citrus to drought stress, based on four input variables including melatonin concentrations, days after applying treatments, citrus species, and level of drought stress, using different Artificial Neural Networks (ANNs) including Generalized Regression Neural Network (GRNN), Radial basis function (RBF), and Multilayer Perceptron (MLP). The results indicated a higher accuracy of GRNN as compared to RBF and MLP. The great accordance between the experimental and predicted data of morphological responses for both training and testing processes support the excellent efficiency of developed GRNN models. Also, GRNN was connected to Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to optimize input variables for obtaining the best morphological responses. Generally, the validation experiment showed that ANN-NSGA-II can be considered as a promising and reliable computational tool for studying and predicting plant morphological and physiological responses to drought stress.

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Section: Plos Onementioning

confidence: 99%

The application of artificial neural networks in modeling and predicting the effects of melatonin on morphological responses of citrus to drought stress

Jafari

Shahsavar

2020

PLoS ONE

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“…Variables can be clustered into two groups, including quantitative (continuous and discrete) and qualitative (ordinal and nominal). Names with two or more classes without a hierarchical order are categorized as nominal variables, while ordinal data have distinct order (level X is more intense than level Y) [65,66]. Counts that include integers are classified as discrete data, while measurements along a continuum, which could be included smaller fractions, are categorized as continuous variables [67].…”

Section: Discussionmentioning

confidence: 99%

Development of support vector machine-based model and comparative analysis with artificial neural network for modeling the plant tissue culture procedures: effect of plant growth regulators on somatic embryogenesis of chrysanthemum, as a case study

et al. 2020

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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 (R 2 > 0.92) had better performance accuracy than MLP (R 2 > 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.

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“…In the 1990s, a wide range of statistical methods for the multivariate analysis of plant cell tissue data were employed, but those studies have some limitations (Gago et al 2010 ; Gallego et al 2011 ): (i) limited kind of data (qualitative or quantitative) can be analyzed using multivariate analysis, but not nominal or image data; (ii) limited application of linear tools such as ANOVA and regression, since biological responses present a high degree of intra- and inter-individual variation that interacts in a non-linear and non-deterministic way; and (iii) a slump in the use of statistical methods to predict or optimize plant tissue culture. In recent years, several parametric approaches such as response surface methodology (RSM), decision trees, Chi-square automatic interaction detector (CHAID), adaptive regression splines and artificial intelligence tools, based on machine learning systems, have been successfully applied to the design of plant tissue media as advanced techniques (Poothong and Reed 2014 ; Akin et al 2016 , 2020 ). Other computer-based tools based on artificial intelligence tools for understanding the effect of media components on in vitro cultured plants were also explored (Gago et al 2010 ; Gallego et al 2011 ; Zielińska and Kępczyńska 2013 ).…”

Section: Unmasking the Effect Of Media Ingredients On Stn Using Artifmentioning

confidence: 99%

Shoot tip necrosis of in vitro plant cultures: a reappraisal of possible causes and solutions

Silva

Nezami-Alanagh

Barreal

et al. 2020

Planta

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Main conclusion Shoot tip necrosis is a physiological condition that negatively impacts the growth and development of in vitro plant shoot cultures across a wide range of species. Abstract Shoot tip necrosis is a physiological condition and disorder that can arise in plantlets or shoots in vitro that results in death of the shoot tip. This condition, which can spread basipetally and affect the emergence of axillary shoots from buds lower down the stem, is due to the cessation of apical dominance. STN can occur at both shoot multiplication and rooting stages. One of the most common factors that cause STN is nutrient deficiency or imbalance. Moreover, the presence or absence of plant growth regulators (auxins or cytokinins) at specific developmental stages may impact STN. The cytokinin to auxin ratio within an in vitro plant can be modified by varying the concentration of cytokinins used in the culture medium. The supply of nutrients to in vitro shoots or plantlets might also affect their hormonal balance, thus modifying the occurrence of STN. High relative humidity within culture vessels and hyperhydricity are associated with STN. An adequate supply of calcium as the divalent cation (Ca2+) can hinder STN by inhibiting the accumulation of phenolic compounds and thus programmed cell death. Moreover, the level of Ca2+ affects auxin transport and ethylene production, and higher ethylene production, which can occur as a result of high relative humidity in or poor ventilation of the in vitro culture vessel, induces STN. High relative humidity can decrease the mobility of Ca2+ within a plant, resulting in Ca2+ deficiency and STN. STN of in vitro shoots or plantlets can be halted or reversed by altering the basal medium, mainly the concentration of Ca2+, adjusting the levels of auxins or cytokinins, or modifying culture conditions. This review examines the literature related to STN, seeks to discover the associated factors and relations between them, proposes practical solutions, and attempts to better understand the mechanism(s) underlying this condition in vitro.

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Analysis of macro nutrient related growth responses using multivariate adaptive regression splines

Cited by 59 publications

References 22 publications

The application of artificial neural networks in modeling and predicting the effects of melatonin on morphological responses of citrus to drought stress

The application of artificial neural networks in modeling and predicting the effects of melatonin on morphological responses of citrus to drought stress

Development of support vector machine-based model and comparative analysis with artificial neural network for modeling the plant tissue culture procedures: effect of plant growth regulators on somatic embryogenesis of chrysanthemum, as a case study

Shoot tip necrosis of in vitro plant cultures: a reappraisal of possible causes and solutions

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