首页    期刊浏览 2025年06月05日 星期四
登录注册

文章基本信息

  • 标题:Early Prediction of Plant Diseases using CNN and GANs
  • 本地全文:下载
  • 作者:Ahmed Ali Gomaa ; Yasser M. Abd El-Latif
  • 期刊名称:International Journal of Advanced Computer Science and Applications(IJACSA)
  • 印刷版ISSN:2158-107X
  • 电子版ISSN:2156-5570
  • 出版年度:2021
  • 卷号:12
  • 期号:5
  • 页码:514
  • DOI:10.14569/IJACSA.2021.0120563
  • 出版社:Science and Information Society (SAI)
  • 摘要:Plant diseases enormously affect the agricultural crop production and quality with huge economic losses to the farmers and the country. This in turn increases the market price of crops and food, which increase the purchase burden of customers. Therefore, early identification and diagnosis of plant diseases at every stage of plant life cycle is a very critical approach to protect and increase the crop yield. In this paper using a deep-learning model, we present a classification system based on real-time images for early identification of plant infection prior of onset of severe disease symptoms at different life stages of a tomato plant infected with Tomato Mosaic Virus (TMV). The proposed classification was applied on each stage of the plant separately to obtain the largest data set and manifestation of each disease stage. The plant stages named in relation to disease stage as healthy (uninfected), early infection, and diseased (late infection). Classification was designed using the Convolutional Neural Network (CNN) model and the accuracy rate was 97%. Using Generative Adversarial Networks (GANs) to increase the number of real-time images and then apply CNN on these new images and the accuracy rate was 98%.
  • 关键词:Plants diseases; deep learning; early detection; convolutional neural network; generative adversarial networks
国家哲学社会科学文献中心版权所有