ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Technical Advances in Plant Science
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1639533
This article is part of the Research TopicPlant Phenotyping for AgricultureView all 7 articles
Field-Deployable Lightweight YOLOv8n for Real-Time Detection and Counting of Maize Seedlings Using UAV RGB Imagery
Provisionally accepted
- Gansu Agricultural University, Lanzhou, China
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The aim of this study is to propose a lightweight YOLOv8n maize seedling detection algorithm that incorporates multi-scale features to address the problems of large number of model parameters and computation, low real-time performance, and small detection range of the existing maize seedling detection models during plant detection. By fusing RepConv with HGNetV2 using the idea of reparameterisation, a Rep_HGBlock structure is designed to form a new lightweight backbone network, Rep_HGNetV2,; BiFPN is introduced into the neck network portion of the model to enhance the interactive fusion of bidirectional information flow between multiple scales and hierarchies; and a fusion task decomposition, dynamic convolutional alignment is designed, DFL (Distribution Focal Loss) ideas, TDADH, a task dynamically aligned detection head, which uses shared convolution and dynamically aligns the tasks of classification and localisation to extract features; and Grad-CAM++ technique is used to generate a heat map for model detection, visualise effective features of the target and understand the model focus region. The experimental results show that the improved model achieves a detection accuracy of 96.5%, which is basically the same as the original model. The weight size, number of parameters, and computational FLOPs are reduced to 3.5 MB, 1.58 M, and 7.4 G, respectively, which are reduced by about 43%, 47%, and 8.6%. The frame rate FPS is only reduced from 149.98 to 146.3, a reduction of about 2.4%. The results show that the lightweight model has high recognition accuracy, speed and low complexity, which is more suitable for practical deployment in resource-constrained edge devices, UAVs, and embedded systems, and is able to provide technical support for the precise management of maize during the seedling stage of drip irrigation water-fertilizer integration.
Keywords: Maize seedling, Small target, Lightweight, YOLOv8n, Grad-CAM++
Received: 02 Jun 2025; Accepted: 20 Aug 2025.
Copyright: © 2025 Feng, Nie and Li. 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: Zhigang Nie, Gansu Agricultural University, Lanzhou, China
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