Image Processing and Artificial Neural Network-Based Models to Measure and Predict Physical Properties of Embryogenic Callus and Number of Somatic Embryos in Ajowan (Trachyspermum ammi (L.) Sprague)
2018
Niazian, Mohsen | Sadat-Noori, SeyedAhmad | Abdipour, Moslem | Tohidfar, Masoud | Mortazavian, SeyedMohammad Mahdi
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.
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