UAV Wheat Rust Detection based on FasterNet-YOLOv8-Reference-Cited by-同舟云学术

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UAV Wheat Rust Detection based on FasterNet-YOLOv8

Published:2023-12-04 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)
language:
Short-container-title:

Author:

Liu Huaiyu¹,Zhang Yueyuan¹,Liu Shuguang²,Zhao Mengyang²,Sun Lining²

Affiliation:

1. Soochow University,School of Mechanical and Electrical Engineering,Department of Automation Engineering,Suzhou,China,215137

2. Soochow University,Robotics and Microsystems Center, School of Mechanical and Electrical Engineering,Suzhou,China,215137

Funder

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10354348/10354529/10354660.pdf?arnumber=10354660

Reference21 articles.

1. Wheat leaf rust resistance gene Lr13 is a specific Ne2 allele for hybrid necrosis

2. Identification and Validation of a Major Quantitative Trait Locus for Adult Plant Resistance Against Leaf Rust From the Chinese Wheat Landrace Bai Qimai

3. Applying convolutional neural networks for detecting wheat stripe rust transmission centers under complex field conditions using RGB-based high spatial resolution images from UAVs

4. Recognition of Weeds in Wheat Fields Based on the Fusion of RGB Images and Depth Images

5. Feasibility of Unmanned Aerial Vehicle Optical Imagery for Early Detection and Severity Assessment of Late Blight in Potato

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1. Improved lightweight flame smoke detection algorithm for YOLOv8n;2024 39th Youth Academic Annual Conference of Chinese Association of Automation (YAC);2024-06-07

2. Multi-Altitude Corn Tassel Detection and Counting Based on UAV RGB Imagery and Deep Learning;Drones;2024-05-14

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