ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Plant Bioinformatics
This article is part of the Research TopicNew Trends in Distributed and Autonomous Intelligent Systems for Crop ProductionView all articles
Garlic-YOLO-DD: A Lightweight Object Detection Algorithm for Garlic Damage Detection
Provisionally accepted
- Changchun University of Science and Technology College of Optical and Electronic Information, Changchun, China
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To address the challenge of applying garlic damage detection models in resource-constrained environments, this study proposes Garlic-YOLO-DD—a lightweight single-stage object detection algorithm based on YOLOv11n. This model effectively resolves the core issues of high computational complexity and excessive parameters in existing methods, achieving efficient and accurate garlic damage recognition suitable for real-time applications. Specifically, replacing conventional convolutional modules in the backbone network with the ADown module significantly reduces parameters and computational load. Simultaneously, integrating the parameter-free SimAM attention mechanism enhances localization and feature extraction capabilities for subtle lesion areas. The efficient BiFPN architecture optimizes the original feature fusion network, improving both speed and effectiveness in multi-scale feature integration. Experiments conducted on a self-built garlic damage dataset demonstrate that the Garlic-YOLO-DD model reduces the number of parameters to 57.96% of YOLOv11n, decreases computational load by 20.63%, increases inference speed by 15.97%, and achieves mAP@50% by 27.64%. This study provides a computer vision solution for automated garlic damage detection in intelligent agricultural systems.
Keywords: Garlic Damage Detection, Lightweight Network, object detection, precision agriculture, YOLO
Received: 09 Sep 2025; Accepted: 08 Dec 2025.
Copyright: © 2025 Gao, Ma, Xia, Qi, Wang, He and Chen. 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: Gang Chen
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