45. Weeds detection in winter wheat field using improved-YOLOv4 with attention module from UAV imagery-Reference-Cited by-同舟云学术

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45. Weeds detection in winter wheat field using improved-YOLOv4 with attention module from UAV imagery

Published:2023-07-02 Issue: Volume: Page:
ISSN:
Container-title:Precision agriculture ’23
language:
Short-container-title:

Author:

Alirezazadeh P.¹,Schirrmann M.¹,Stolzenburg F.²

Affiliation:

1. Leibniz Institute for Agricultural Engineering and Bioeconomy, Department of Agromechatronics, Max-Eyth-Allee 100, 14469 Potsdam, Germany.

2. Automation and Computer Sciences Department, Harz University of Applied Sciences, Friedrichstr. 57-59, 38855 Wernigerode, Germany.

Publisher

Wageningen Academic Publishers

Link

https://www.wageningenacademic.com/doi/pdf/10.3920/978-90-8686-947-3_45

Reference13 articles.

1. Alirezazadeh, P., Schirrmann, M. & Stolzenburg, F. (2022). Improving Deep Learning-based Plant Disease Classification with Attention Mechanism. Gesunde Pflanzen, 79 49-59. https://doi.org/10.1007/s10343022-00796-y

2. Improved digital image-based assessment of soil aggregate size by applying convolutional neural networks

3. Drone and sensor technology for sustainable weed management: a review

4. A survey of deep learning techniques for weed detection from images

5. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Agricultural weed identification in images and videos by integrating optimized deep learning architecture on an edge computing technology;Computers and Electronics in Agriculture;2024-01

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