Diagnosis of Marine Aquaculture Diseases Based on VGG-16 Convolutional Neural Network
CSTR:
Author:
  • Article
  • | |
  • Metrics
  • |
  • Reference [8]
  • |
  • Related [20]
  • | | |
  • Comments
    Abstract:

    Marine aquaculture is affected by a variety of diseases, and the differences in lesion characteristics are very suitable for image recognition. Based on the above requirements, this study designs a marine breeding disease diagnosis model based on VGG-16 convolutional neural network, and uses a stochastic gradient descent algorithm and overfitting prevention technology to improve the model. The experimental results show that this model is better than other traditional network models, and has high recognition accuracy, generalization ability, and robustness. It can accurately and quickly diagnose diseases with certain expansion and promotion value.

    Reference
    [1] 叶婵, 邓长辉, 曹向南, 等. 基于BP神经网络的对虾病害防治专家系统. 大连海洋大学学报, 2015, 30(3): 319-323
    [2] 宋健, 孙学岩. 基于形状特征的鱼病诊断图像检索系统. Proceedings of 2010 International Conference on Circuit and Signal Processing & 2010 Second IITA International Joint Conference on Artificial Intelligence(Volume 2). Shanghai, China. 2010. 265-268.
    [3] Malik S, Kumar T, Sahoo AK. Image processing techniques for identification of fish disease. Proceedings of the 2017 IEEE 2nd International Conference on Signal and Image Processing. Singapore. 2017. 55-59.
    [4] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv: 1409.1556, 2014.
    [5] Bottou L. Large-scale machine learning with stochastic gradient descent. Proceeding of the 19th International Conference on Computational Statistics. Paris, France. 2010. 177-186.
    [6] Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 2014, 15(1): 1929-1958
    [7] Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv: 1502.03167, 2015.
    [8] Pan SJ, Yang Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345-1359. [doi: 10.1109/TKDE.2009.191
    Cited by
    Comments
    Comments
    分享到微博
    Submit
Get Citation

李海涛,王腾,王印庚.基于VGG-16卷积神经网络的海水养殖病害诊断.计算机系统应用,2020,29(7):222-227

Copy
Share
Article Metrics
  • Abstract:1269
  • PDF: 1887
  • HTML: 1669
  • Cited by: 0
History
  • Received:November 22,2019
  • Revised:December 16,2019
  • Online: July 04,2020
  • Published: July 15,2020
Article QR Code
You are the first990433Visitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-3
Address:4# South Fourth Street, Zhongguancun,Haidian, Beijing,Postal Code:100190
Phone:010-62661041 Fax: Email:csa (a) iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063