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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1449514

An Improved U-Net and Attention Mechanism-Based Model for Sugar Beet and Weed Segmentation

Provisionally accepted
Yadong Li Yadong Li 1Li Rujia Li Rujia 2Rongbiao Ji Rongbiao Ji 1Mengyao Wu Mengyao Wu 1娇娇 陈 娇娇 陈 1Yehui Wu Yehui Wu 1Dinghao Chen Dinghao Chen 3Cong Han Cong Han 1Ruilin Han Ruilin Han 1Yongxiu Liu Yongxiu Liu 1Yuwen Ruan Yuwen Ruan 4Jianping Yang Jianping Yang 1*
  • 1 Yunnan Agricultural University, Kunming, China
  • 2 Zhejiang University of Finance and Economics, Hangzhou, Zhejiang Province, China
  • 3 Baoshan University, Baoshan, Yunnan, China
  • 4 University of Copenhagen, Copenhagen, Capital Region of Denmark, Denmark

The final, formatted version of the article will be published soon.

    Weeds are a major factor affecting crop yield and quality. Accurate identification and localization of crops and weeds are essential for achieving automated weed management in precision agriculture, especially given the challenges in recognition accuracy and real-time processing in complex field environments. To address this issue, this paper proposes an sugar beet and weed segmentation model based on an improved UNet architecture and attention mechanisms to enhance both recognition accuracy and processing speed. The model adopts the encoder-decoder structure of UNet, utilizing MaxViT (Multi-Axis Vision Transformer) as the encoder to capture both global and local features within images. Additionally, CBAM (Convolutional Block Attention Module) is incorporated into the decoder as a multi-scale feature fusion module, adaptively adjusting feature map weights to enable the model to focus more accurately on the edges and textures of sugar beets and weeds. Experimental results show that the proposed model achieved 84.28% mIoU and 88.59% mPA on the sugar beet dataset, representing improvements of 3.08% and 3.15% over the baseline UNet model, respectively, and outperforming mainstream models such as FCN, PSPNet, SegFormer, DeepLabv3+, and HRNet. Moreover, the model's inference time is only 0.0559 seconds, reducing computational overhead while maintaining high accuracy. Its performance on a sunflower dataset further verifies the model's generalizability and robustness. This study, therefore, provides an efficient and accurate solution for sugar beet and weed segmentation, laying a foundation for future research on automated crop and weed identification..

    Keywords: Semantic segmentation, UNET, deep learning, MaxViT, CBAM, attention mechanism, image processing, Multi-scale features

    Received: 15 Jun 2024; Accepted: 19 Dec 2024.

    Copyright: © 2024 Li, Rujia, Ji, Wu, 陈, Wu, Chen, Han, Han, Liu, Ruan and Yang. 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: Jianping Yang, Yunnan Agricultural University, Kunming, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.