Aphid-YOLO: A lightweight detection model for real-time identification and counting of aphids in complex field environments

Zheng, Y., Qi, J., Yang, Y. et al. (2 more authors) (Accepted: 2025) Aphid-YOLO: A lightweight detection model for real-time identification and counting of aphids in complex field environments. IEEE Transactions on AgriFood Electronics. ISSN: 2771-9529 (In Press)

Abstract

Metadata

Item Type: Article
Authors/Creators:
Copyright, Publisher and Additional Information:

© 2025 IEEE

Keywords: YOLOv8, Lightweight; Aphid Detection; Tiny Object Detection; Deep Learning
Dates:
  • Accepted: 11 August 2025
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Funding Information:
Funder
Grant number
INNOVATE UK
10050919 TS/X014096/1
Depositing User: Symplectic Sheffield
Date Deposited: 19 Aug 2025 14:49
Last Modified: 19 Aug 2025 14:49
Status: In Press
Publisher: Institute of Electrical and Electronics Engineers
Refereed: Yes
Open Archives Initiative ID (OAI ID):

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