ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Technical Advances in Plant Science
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1672425
This article is part of the Research TopicPlant Phenotyping for AgricultureView all 9 articles
Research on Detection and Counting Method of Wheat Ears in the Field Based on YOLOv11-EDS
Provisionally accepted
- 1Liaocheng University, Liaocheng, China
- 2Shandong Academy of Agricultural Sciences, Jinan, China
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Word count: 269 Introduction: As a major food crop, accurate detection and counting of wheat ears in the field are of great significance for yield estimation. Aiming at the problems of low detection accuracy and large computational load of existing detection and counting methods in complex farmland environments, this study proposes a lightweight wheat ear detection model, YOLOv11-EDS. Methods: First, the Dysample dynamic upsampling operator is introduced to optimize the upsampling process of feature maps and enhance feature information transmission. Second, the Efficient Channel Attention (ECA) mechanism is introduced to make the model focus more on key features and improve the ability to capture wheat ear features. Finally, the Slim-Neck module is introduced to optimize the feature fusion structure and enhance the model's processing capability for features of different scales. Results: Experimental results show that the performance of the improved YOLOv11-EDS model is significantly improved on the global wheat ear dataset. The precision is increased by 1.8 percentage points, the recall by 3.4 percentage points, mAP@0.5 by 1.5 percentage points, and mAP@0.5:0.95 by 2.4 percentage points compared with the baseline model YOLOv11. Meanwhile, the model parameters are reduced to 2.5 M, and the floating-point operations are reduced to 5.8 G, which are 0.1 M and 0.5 G lower than the baseline model, respectively, achieving dual optimization of accuracy and efficiency. The model still demonstrates excellent detection performance on a self-built iPhone-view wheat ear datasets, fully verifying its robustness and environmental adaptability. Discussion: This study provides an efficient solution for the automated analysis of wheat phenotypic parameters in complex farmland environments, which is of great value for promoting the development of smart agriculture.
Keywords: Wheat ear, object detection, DySample, Lightweight, YOLOv11
Received: 24 Jul 2025; Accepted: 27 Aug 2025.
Copyright: © 2025 Li, Wang, Luo, Feng, Gong, Zhang and Zheng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jiye Zheng, Shandong Academy of Agricultural Sciences, Jinan, China
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