Application of image analysis with neural network for plant somatic embryo culture
1993
Uozumi, N. (Nagoya Univ. (Japan). Faculty of Engineering) | Yoshino, T. | Shiotani, S. | Suehara, K. | Arai, F. | Fukuda, T. | Kobayashi, T.
A method of classifying celery embryos and nonembryos using image analysis with a neural network to decide the time for transfer to the next culture stage in plant somatic embryo culture is presented. Since the image database of celery cells is vast, four key input parameters (area, ratio of length to width, circularity and distance dispersion) were selected. Among these four parameters, use of the first three was found to be satisfactory for classification between embryos and nonembryos. Using the three parameters, the trained neural network was also able to classify globular, heart- and torpedo-shaped embryos at a level comparable with estimation by a human expert. By using the trained neural network, the number of plantlets that would be formed after the second regeneration culture of 14d could be successfully predicted from number of heart- and torpedo-shaped embryos at the end of the first regeneration stage
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