Image Processing and Artificial Neural Network-Based Models to Measure and Predict Physical Properties of Embryogenic Callus and Number of Somatic ... View Full Text


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Article Info

DATE

2018-01-09

AUTHORS

Mohsen Niazian, Seyed Ahmad Sadat-Noori, Moslem Abdipour, Masoud Tohidfar, Seyed Mohammad Mahdi Mortazavian

ABSTRACT

Trachyspermum ammi (L.) Sprague (Ajowan) is an endangered medicinal plant with useful pharmaceutical properties. Ex situ conservation of this medicinal plant needs the development of an in vitro regeneration protocol using somatic embryogenesis. In the present study, a high-precision image-processing approach was successfully applied to measure physical properties of embryogenic callus. Explant age and the concentrations of 2,4-dichlorophenoxyacetic acid (2,4-D), kinetin (Kin), and sucrose were used as inputs, and an artificial intelligence technique was applied to predict physical properties of embryogenic callus, and the number of somatic embryos produced. Artificial neural network (ANN) models were tested to find the best combinations of input variables that affected output variables. The lower values of root mean square error, and mean absolute error, and the highest values of determination coefficient, were achieved when all four input variables were applied to predict the number of somatic embryos, the area of the callus, the perimeter of the callus, the Feret diameter of the callus, the roundness of the callus, and the true density of the callus in ANN models. The highest measured and predicted number of somatic embryos were achieved from the interaction of 15-d-old explants × 1.5 mg L−1 2,4-D × 0.5 mg L−1 Kin × 2.5% (w/v) sucrose. Based on sensitivity analysis, the 2,4-D concentration was the most important component in the culture medium that affected the number of somatic embryos and physical properties of the embryogenic callus tissue. More... »

PAGES

54-68

References to SciGraph publications

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  • 2008-06-26. Efficiency of neural networks for prediction of in vitro culture conditions and inoculum properties for optimum productivity in PLANT CELL, TISSUE AND ORGAN CULTURE (PCTOC)
  • 1993-02. Image processing: a non-destructive method for measuring growth in cell and tissue culture in PLANT CELL REPORTS
  • 2015-02-20. Effects of length and position of hypocotyl explants on Cuminum cyminum L. callogensis by image processing analysis in PLANT CELL, TISSUE AND ORGAN CULTURE (PCTOC)
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  • 2015-09-16. Influence of plant growth regulators and spermidine on somatic embryogenesis and plant regeneration in four Indian genotypes of finger millet (Eleusine coracana (L.) Gaertn) in PLANT CELL, TISSUE AND ORGAN CULTURE (PCTOC)
  • 2011-05-18. Estimation of rice neck blasts severity using spectral reflectance based on BP-neural network in ACTA PHYSIOLOGIAE PLANTARUM
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  • 1999-01. A neural network based pattern recognition system for somatic embryos of Douglas fir in PLANT CELL, TISSUE AND ORGAN CULTURE (PCTOC)
  • 2014-02-26. Design of tissue culture media for efficient Prunus rootstock micropropagation using artificial intelligence models in PLANT CELL, TISSUE AND ORGAN CULTURE (PCTOC)
  • 2017-06-01. Effect of colchicine-induced polyploidy on morphological characteristics and essential oil composition of ajowan (Trachyspermum ammi L.) in PLANT CELL, TISSUE AND ORGAN CULTURE (PCTOC)
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    http://scigraph.springernature.com/pub.10.1007/s11627-017-9877-7

    DOI

    http://dx.doi.org/10.1007/s11627-017-9877-7

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