Research on improvement strategies for a lightweight multi-object weed detection network based on YOLOv5-Reference-Cited by-同舟云学术

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Research on improvement strategies for a lightweight multi-object weed detection network based on YOLOv5

Published:2024-12 Issue: Volume:186 Page:106912
ISSN:0261-2194
Container-title:Crop Protection
language:en
Short-container-title:Crop Protection

Author:

Sun Jiandong^ORCID,You Jinlong,Li Fengmei,Sun Jianhong,Yang Mengjiao,Zhao Xueguan,Jin Ning,Bai Haoran^ORCID

Publisher

Elsevier BV

Reference28 articles.

1. Detection of tomato plant phenotyping traits using YOLOv5-based single stage detectors;Cardellicchio;Comput. Electron. Agric.,2023

2. Weed detection in sesame fields using a YOLO model with an enhanced attention mechanism and feature fusion;Chen;Comput. Electron. Agric.,2022

3. Performance evaluation of deep transfer learning on multi-class identification of common weed species in cotton production systems;Chen;Comput. Electron. Agric.,2022

4. Natural language processing;Chowdhary;Fundam. Artif. Intell.,2020

5. A review of semantic segmentation using deep neural networks;Guo;Int. J. Multimed. Inf. Retr.,2018

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