Agricultural Pest Super-Resolution and Identification With Attention Enhanced Residual and Dense Fusion Generative and Adversarial Network
The growth of the most significant field crops such as rice, wheat, maize, and soybean are influenced because of various pests. And crop production is decreased due to various categories of insects. Deep learning technologies significantly increased the efficiency of identifying and controlling agri...
Main Authors: | Qiang Dai, Xi Cheng, Yan Qiao, Youhua Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9082695/ |
Similar Items
-
Crop Leaf Disease Image Super-Resolution and Identification With Dual Attention and Topology Fusion Generative Adversarial Network
by: Qiang Dai, et al.
Published: (2020-01-01) -
Panchromatic Image Super-Resolution via Self Attention-Augmented Wasserstein Generative Adversarial Network
by: Juan Du, et al.
Published: (2021-03-01) -
Remote Sensing Image Super-Resolution Based on Dense Channel Attention Network
by: Yunchuan Ma, et al.
Published: (2021-07-01) -
Remote Sensing Image Super-Resolution via Residual Aggregation and Split Attentional Fusion Network
by: Long Chen, et al.
Published: (2021-01-01) -
Deep Residual Dual-Attention Network for Super-Resolution Reconstruction of Remote Sensing Images
by: Bo Huang, et al.
Published: (2021-07-01)