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. 2025 Jan 7:15:1503033.
doi: 10.3389/fpls.2024.1503033. eCollection 2024.

YOLOv7-DWS: tea bud recognition and detection network in multi-density environment via improved YOLOv7

Xiaoming Wang  1   2 , Zhenlong Wu  3 , Guannan Xiao  1   2 , Chongyang Han  3 , Cheng Fang  3
Affiliations

YOLOv7-DWS: tea bud recognition and detection network in multi-density environment via improved YOLOv7

Xiaoming Wang et al. Front Plant Sci. .

Abstract

Introduction: Accurate detection and recognition of tea bud images can drive advances in intelligent harvesting machinery for tea gardens and technology for tea bud pests and diseases. In order to realize the recognition and grading of tea buds in a complex multi-density tea garden environment.

Methods: This paper proposes an improved YOLOv7 object detection algorithm, called YOLOv7-DWS, which focuses on improving the accuracy of tea recognition. First, we make a series of improvements to the YOLOv7 algorithm, including decouple head to replace the head of YOLOv7, to enhance the feature extraction ability of the model and optimize the class decision logic. The problem of simultaneous detection and classification of one-bud-one-leaf and one-bud-two-leaves of tea was solved. Secondly, a new loss function WiseIoU is proposed for the loss function in YOLOv7, which improves the accuracy of the model. Finally, we evaluate different attention mechanisms to enhance the model's focus on key features.

Results and discussion: The experimental results show that the improved YOLOv7 algorithm has significantly improved over the original algorithm in all evaluation indexes, especially in the R Tea (+6.2%) and mAP@0.5 (+7.7%). From the results, the algorithm in this paper helps to provide a new perspective and possibility for the field of tea image recognition.

Keywords: YOLOv7; deep learning; images recognition; multi-density; object detection; tea buds.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Diagram of different types of picking Yinghong No. 9 tea leaves. (A) one-bud-one-leaf; (B) one-bud-two-leaves.
Figure 2
Figure 2
Multi-density images of a partial tea dataset.
Figure 3
Figure 3
Network structure of YOLOv7-DWS model.
Figure 4
Figure 4
Decoupled head structure schematic.
Figure 5
Figure 5
A PyTorch way of implementing SimAM.
Figure 6
Figure 6
Visual validation of different attention mechanisms.
Figure 7
Figure 7
Visual validation of different attention mechanisms.
Figure 8
Figure 8
Visual verification of tea detection at different densities.
See this image and copyright information in PMC

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