首页    期刊浏览 2024年11月26日 星期二
登录注册

文章基本信息

  • 标题:Convolutional variational autoencoder-based feature learning for automatic tea clone recognition
  • 本地全文:下载
  • 作者:Vicky Zilvan ; Ade Ramdan ; Ana Heryana
  • 期刊名称:Journal of King Saud University @?C Computer and Information Sciences
  • 印刷版ISSN:1319-1578
  • 出版年度:2022
  • 卷号:34
  • 期号:6
  • 页码:3332-3342
  • 语种:English
  • 出版社:Elsevier
  • 摘要:It is common to have various clones from cross-seedlings or unintended planting by the farmers in a tea plantation. Since each tea clone has distinctive features such as quality, resistance to diseases, etc., visual inspections are usually conducted on the plantations to segment areas with different tea clones within the plantation to produce crops with consistent quality. However, this would be costly and time-consuming. In this work, we apply machine learning and develop an application to recognize tea clones automatically. We propose a convolutional variational autoencoder-based feature learning algorithm to produce robust features against data distortions. There are two main advantages of using this algorithm for feature learning. First, there is no need to design complex handcrafted features for classifications, usually conducted in machine learning. Second, the resulting features are more robust when tested with data taken from unideal conditions. The proposed method is evaluated using the original and the distorted image. Our proposed method achieves the best performance of 0.83 (83%) for the original image test, 0.75 (75%) for the gaussian blur image test, and 0.78 (78%) for the median blur image test. This is a much more robust result than VGGNet16, a popular supervised deep convolutional neural network.
国家哲学社会科学文献中心版权所有