Annotation-efficient weed detection using DINOv3-distilled YOLOv12.

Authors

Saif Khan, Osama Bin Qashem, Mahmudul Hasan Hamim, Nusrat Jahan Oishi, Raiyan Gani, Mohammad Rifat Ahmmad Rashid, Mahamudul Hasan, Raihan Ul Islam

Published in

Scientific reports. Jul 03, 2026. Epub Jul 03, 2026.

Abstract

Weed pressure causes global crop yield losses of 10-34%, while the deployment of deep learning-based weed detection systems at scale remains constrained by the high cost of bounding-box annotation across diverse field environments. This study addresses this annotation bottleneck in precision agriculture by proposing WEEDINO-YOLOv12, a label-efficient weed detection framework that transfers global-average-pooled feature distributions from a frozen DINOv3 ViT-B/16 teacher into a lightweight YOLOv12n backbone through feature-distribution distillation on unlabeled agricultural imagery, followed by supervised fine-tuning on a limited labeled subset. To rigorously evaluate the proposed framework, we present a controlled empirical benchmark comparing four training regimes: fully supervised YOLOv12n, semi-supervised Soft Teacher, self-supervised BYOL, and the proposed DINOv3 distillation approach. All methods are assessed using a common YOLOv12n backbone, consistent evaluation metrics, matched controls, and multi-seed reporting. External validation on the multi-class CottonWeedDet12 dataset further examines whether the observed label-efficient behaviour generalises beyond the single-class Roboflow Weeds benchmark. Across matched 20%-label settings, WEEDINO-YOLOv12 improved [email protected]:0.95 from 0.6402 ± 0.0271 to 0.6517 ± 0.0087 on the Roboflow fixed split and from 0.7987 ± 0.0154 to 0.8083 ± 0.0078 on CottonWeedDet12. Full-label supervision remained the strongest overall setting, indicating that the proposed method provides modest but consistent annotation-efficiency gains rather than replacing fully supervised training. High-resolution fine-tuning at 896 × 896 pixels is analysed separately because it can improve localisation independently of the distillation stage. A Streamlit-based deployment prototype further demonstrates the practical accessibility of the framework for agronomists and precision-agriculture users without requiring direct interaction with deep learning code.

PMID:
42399661
Bibliographic data and abstract were imported from PubMed on 04 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.