Cotton Fusarium wilt diagnosis based on generative adversarial networks in small samples
This study aimed to explore the feasibility of applying Generative Adversarial Networks (GANs) for the diagnosis of Verticillium wilt disease in cotton and compared it with traditional data augmentation methods and transfer learning. By designing a model based on small-sample learning, we proposed a...
Main Authors: | Zhenghang Zhang, Lulu Ma, Chunyue Wei, Mi Yang, Shizhe Qin, Xin Lv, Ze Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2023-12-01
|
Series: | Frontiers in Plant Science |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2023.1290774/full |
Similar Items
-
Early Monitoring of Cotton Verticillium Wilt by Leaf Multiple “Symptom” Characteristics
by: Mi Yang, et al.
Published: (2022-10-01) -
A study on cotton yield prediction based on the chlorophyll fluorescence parameters of upper leaves
by: Yiren DING, et al.
Published: (2022-09-01) -
Estimation of Cotton Nitrogen Content Based on Multi-Angle Hyperspectral Data and Machine Learning Models
by: Xiaoting Zhou, et al.
Published: (2023-02-01) -
A Deep Convolutional Generative Adversarial Networks-Based Method for Defect Detection in Small Sample Industrial Parts Images
by: Hongbin Gao, et al.
Published: (2022-06-01) -
Regulatory Network of Cotton Genes in Response to Salt, Drought and Wilt Diseases (Verticillium and Fusarium): Progress and Perspective
by: Masum Billah, et al.
Published: (2021-11-01)