Application of Artificial Neural Network for Modeling and Studying In Vitro Genotype-Independent Shoot Regeneration in Wheat

Hesami, Mohsen; Condori-Apfata, Jorge A.; Valencia, Maria Valderrama; Mohammadi, Mohsen

doi:10.3390/app10155370

Cited by 41 publications

(31 citation statements)

References 54 publications

(90 reference statements)

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“…In silico predictive deep learning algorithms such as artificial neural networks can process large and diverse datasets and provide high-throughput solutions [50,51]. These techniques have been utilized to optimize regeneration in wheat [52], Chrysanthemum spp. L. [53,54], Swertia paniculata (Wall.)…”

Section: Discussionmentioning

confidence: 99%

Advancements in Low-Chill Blueberry Vaccinium corymbosum L. Tissue Culture Practices

Cappai

Garcia

Cullen

et al. 2020

Plants

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The demand for blueberry Vaccinium corymbosum L. (and hybrids) plants has significantly increased in the last 30 years due to its market expansion. In vitro propagation of sterile plants are required for commercial purposes but also for research applications such as plant transformation. Thus far, tissue culture characteristics of the tropical-adapted blueberry have been scarcely studied. In this study we present the following findings: (i) zeatin, a hormone used to promote plant growth, should be used in the 1–2 mg/L range to promote plant architecture optimal for transformation experiments; (ii) red-blue LED lights induce more production of meristems and biomass than white LED or fluorescent lights; (iii) levels as high as 1000 mg/L of decontamination agents (the antibiotics timentin and cefotaxime) can be used to eliminate Agrobacterium overgrowth without inhibiting plant growth during plant transformation experiments; (iv) kanamycin, paromomycin, and geneticin, which are widely used antibiotics to select transgene-carrying transformants, cannot be efficiently used in this system; (v) glufosinate, a widely used herbicide, shows potential to be used as an effective selectable marker for transformed plants.

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Section: Discussionmentioning

confidence: 99%

Advancements in Low-Chill Blueberry Vaccinium corymbosum L. Tissue Culture Practices

Cappai

Garcia

Cullen

et al. 2020

Plants

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“…Plant biological data can be categorized as ordinal (plant quality rated as weak, moderate, and good), nominal (type of morphological responses such as normal and necrosis), continuous (height of shoots or roots), and discrete (number of leaf). Traditional linear methods such as regression and ANOVA must be just applied with continuous variables that demonstrate a linear relationship between the explanatory and dependent variables [23,61,62]. Hence, the conventional computational approaches are not appropriate for analyzing plant biological processes [57].…”

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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“…Analyzing large datasets consisting of spectral reflectance data requires intensive computational and statistical analyses, which is still challenging in many plant breeding programs ( Lopez-Cruz et al, 2020 ). Nowadays, machine learning algorithms have drawn attention from researchers to develop model-based breeding methods that can improve the efficiency of breeding processes ( Hesami et al, 2020a ). Recently, one of the most common artificial neural networks (ANNs), the multilayer perceptron (MLP) developed by Pal and Mitra (1992) , has been broadly used for modeling and predicting complex traits, such as yield, in different breeding programs ( Geetha, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean

Yoosefzadeh-Najafabadi

et al. 2021

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Recent substantial advances in high-throughput field phenotyping have provided plant breeders with affordable and efficient tools for evaluating a large number of genotypes for important agronomic traits at early growth stages. Nevertheless, the implementation of large datasets generated by high-throughput phenotyping tools such as hyperspectral reflectance in cultivar development programs is still challenging due to the essential need for intensive knowledge in computational and statistical analyses. In this study, the robustness of three common machine learning (ML) algorithms, multilayer perceptron (MLP), support vector machine (SVM), and random forest (RF), were evaluated for predicting soybean (Glycine max) seed yield using hyperspectral reflectance. For this aim, the hyperspectral reflectance data for the whole spectra ranged from 395 to 1005 nm, which were collected at the R4 and R5 growth stages on 250 soybean genotypes grown in four environments. The recursive feature elimination (RFE) approach was performed to reduce the dimensionality of the hyperspectral reflectance data and select variables with the largest importance values. The results indicated that R5 is more informative stage for measuring hyperspectral reflectance to predict seed yields. The 395 nm reflectance band was also identified as the high ranked band in predicting the soybean seed yield. By considering either full or selected variables as the input variables, the ML algorithms were evaluated individually and combined-version using the ensemble–stacking (E–S) method to predict the soybean yield. The RF algorithm had the highest performance with a value of 84% yield classification accuracy among all the individual tested algorithms. Therefore, by selecting RF as the metaClassifier for E–S method, the prediction accuracy increased to 0.93, using all variables, and 0.87, using selected variables showing the success of using E–S as one of the ensemble techniques. This study demonstrated that soybean breeders could implement E–S algorithm using either the full or selected spectra reflectance to select the high-yielding soybean genotypes, among a large number of genotypes, at early growth stages.

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Application of Artificial Neural Network for Modeling and Studying In Vitro Genotype-Independent Shoot Regeneration in Wheat

Cited by 41 publications

References 54 publications

Advancements in Low-Chill Blueberry Vaccinium corymbosum L. Tissue Culture Practices

Advancements in Low-Chill Blueberry Vaccinium corymbosum L. Tissue Culture Practices

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

Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean

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