A pattern recognition system was developed to classify Douglas fir somatic embryos by employing an image analysis system and two neural network based classifiers. The contour of embryo images was segmented, digitalized and converted to numerical values after the discrete and fast Fourier transformation. These values, or Fourier features, along with some other shape factors, were used for embryo classification. The pattern recognition system used a hierarchical decision tree to classify Douglas fir embryos into three normal and one abnormal embryo classes. An accuracy of greater than 80% was achieved for normal embryos. This system provides an objective and efficient method of classifying embryos of Douglas fir. It will be a useful tool for kinetic studies and process optimization of conifer somatic embryogenesis.
Bibliographical noteFunding Information:
This work was supported in part by grants from the National Science Foundation (BCS 9015817 and BES-93 21426) and the Minnesota Supercomputer Institute.
Copyright 2004 Elsevier Science B.V., Amsterdam. All rights reserved.
- Douglas fir
- Image analysis
- Neural network
- Pattern recognition
- Pseudotsuga menziesii
- Somatic embryogenesis