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
Aphids are among the most destructive pests that threaten global crop yields, harming crops through feeding and virus transmission. Accurate detection of aphids in fields is a crucial step to implement sustainable agricultural pest management. However, their tiny size of aphids and the complex image background present significant challenges for accurate identification and classification for in-field detection. In response to the challenges, this study proposes a lightweight real-time object detection model, Aphid-YOLO (A-YOLO), for in-field aphid identification and counting. Specifically, a Tiny Path Aggregation Network with C2f-CG modules is proposed to enhance the detection ability of tiny objects while maintaining a low computational cost through efficiently fusing multi-layer features. For model training, Normalized Wasserstein Distance loss function is adopted to address the optimization challenges caused by the tiny size of aphids. Additionally, an optimized data augmentation method, Mosaic9, is introduced to enrich training samples and positive supervised signals for addressing the classif ication challenge of tiny aphids. To validate the effectiveness of A-YOLO, this study conducts comprehensive experiments on an aphid detection dataset with images collected by handheld devices from complex field environment. Experimental results demonstrate that A-YOLO achieves outstanding detection efficiency, with an mAP@0.5 of 83.4%, an mAP@0.5:0.95 of 33.7%, an inference speed of 72 FPS, and a model size of 30.6 MB. Compared to the YOLOv8m model employing traditional Mosaic data augmentation, the proposed method improves mAP@0.5 by 5.8%, mAP@0.5:0.95 by 2.7%, increases inference speed by 5 FPS, and reduces model size by 38.4%.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2025 IEEE |
Keywords: | YOLOv8, Lightweight; Aphid Detection; Tiny Object Detection; Deep Learning |
Dates: |
|
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): | oai:eprints.whiterose.ac.uk:230321 |
Download
Filename: Trans_Agrifood_Aphid_YOLO (22).pdf
