ORIGINAL RESEARCH article

Front. Plant Sci.

Sec. Technical Advances in Plant Science

Volume 16 - 2025 | doi: 10.3389/fpls.2025.1614881

This article is part of the Research TopicMachine Vision and Machine Learning for Plant Phenotyping and Precision Agriculture, Volume IIView all 36 articles

A Detection Method for Synchronous Recognition of String Tomatoes and Picking Points Based on Keypoint Detection

Provisionally accepted
  • 1College of Software, Shanxi Agricultural University, Jinzhong, Shanxi Province, China
  • 2Shanxi Agricultural University, Jinzhong, China

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

In the greenhouse environment, factors such as variable lighting conditions, the similarity in color between fruit stems and background, and the complex growth posture of string tomatoes lead to low detection accuracy for picking points. This paper proposes a detection method for the synchronous recognition of tomatoes and their picking points based on keypoint detection. Using YOLOv8n-pose as the baseline model, we constructed the YOLOv8-TP model. To reduce the computational load of the model, we replaced the C2f module in the backbone network with the C2f-OREPA module. To enhance the model's accuracy and performance, we introduced a PSA mechanism after the backbone network. Additionally, to strengthen the model's feature extraction capabilities, we incorporated CGAFusion at the end of the Neck, which adaptively emphasizes important features while suppressing less important ones, thereby enhancing feature expressiveness. Experimental results show that the YOLOv8-TP model achieved an accuracy of 89.8% in synchronously recognizing tomatoes and picking points, with an inference speed of 154.7 FPS. The YOLOv8n-pose model achieves an inference speed of 148.6 FPS. Compared to the baseline model, YOLOv8-TP improved precision, mAP@.5, mAP@.5:.95, and F1-score by 0.6%, 1%, 2%, and 1%, respectively, while reducing model complexity by 8.1%. The Euclidean distance error for detecting picking points was less than 25 pixels, and the depth error was less than 3 millimeters. This method demonstrates excellent detection performance and provides a reference model for detecting string tomatoes and their picking points.

Keywords: keypoint detection, YOLOv8, String Tomato, Picking point, PSA Mechanism

Received: 20 Apr 2025; Accepted: 03 Jul 2025.

Copyright: © 2025 Deng, Ma, Chen and Song. 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: GuoZhu Song, Shanxi Agricultural University, Jinzhong, China

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