Pattern recognition method for the identification of Daiqu large yellow croaker based on computer vision
A pattern recognition method based on computer vision was developed for the identification of Daiqu large yellow croaker. First, 24 morphological parameters of Daiqu large yellow croaker were measured for both F<sub>2</sub> and F<sub>3</sub> generations by computer vision tec...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Zhejiang University Press
2018-07-01
|
| Series: | 浙江大学学报. 农业与生命科学版 |
| Subjects: | |
| Online Access: | https://www.academax.com/doi/10.3785/j.issn.1008-9209.2018.07.030 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | A pattern recognition method based on computer vision was developed for the identification of Daiqu large yellow croaker. First, 24 morphological parameters of Daiqu large yellow croaker were measured for both F<sub>2</sub> and F<sub>3</sub> generations by computer vision technology. Then principal component analysis (PCA) and successive projections algorithm (SPA) were respectively applied to extract and select the measured morphological parameters, and then obtained three groups of different characteristic variable sets, which were PCA transformed feature, PCA selected feature, SPA selected feature, respectively. Finally, respectively. Finally, sparse representation (SR) models were built for identificating the F<sub>2</sub> and F<sub>3</sub> generations of Daiqu large yellow croaker by using the extracted morphological features. The results indicated that the main morphological features for the identification of Daiqu large yellow croaker were total length/body length, total length/head length, total length/ caudal peduncle length, body length/head length and caudal peduncle length/caudal peduncle height. The results of SR models showed that the three groups of characteristic variable can effectively identify the F<sub>2</sub> and F<sub>3</sub> generations of Daiqu large yellow croaker, with the average recognition accuracy of 88.3%, 79.0% and 80.5%; among them, the best SR model with PCA transformed feature achieved an average accuracy of 88.3% for the identification of Daiqu large yellow croaker. This study provides an effective way to establish shape index and carry out shape evaluation research of Daiqu large yellow croaker. |
|---|---|
| ISSN: | 1008-9209 2097-5155 |