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METHODS article

Front. Plant Sci.
Sec. Technical Advances in Plant Science
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1523552
This article is part of the Research Topic Optimizing Deep Learning for Effective Plant Species Recognition and Conservation View all 11 articles

DPD-YOLO: Dense Pineapple Fruit Target Detection Algorithm in Complex Environments Based on YOLOv8 Combined with Attention Mechanism

Provisionally accepted
Cong Lin Cong Lin 1Wencheng Jiang Wencheng Jiang 1*Weiye Zhao Weiye Zhao 1*Zhong Xue Zhong Xue 2*Lilan Zou Lilan Zou 1*
  • 1 School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, China
  • 2 South Subtropical Crops Research Institute, Chinese Academy of Tropical Agricultural Sciences, Zhanjiang, Guangdong Province, China

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

    With the development of deep learning technology and the widespread application of drones in the agricultural sector, the use of computer vision technology for target detection of pineapples has gradually been recognized as one of the key methods for estimating pineapple yield. When images of pineapple fields are captured by drones, the fruits are often obscured by the pineapple leaf crowns due to their appearance and planting characteristics. Additionally, the background in pineapple fields is relatively complex, and current mainstream target detection algorithms are known to perform poorly in detecting small targets under occlusion conditions in such complex backgrounds. To address these issues, an improved YOLOv8 target detection algorithm, named DPD-YOLO (Dense-Pineapple-Detection YOU Only Look Once), has been proposed for the detection of pineapples in complex environments. The DPD-YOLO model is based on YOLOv8 and introduces the attention mechanism (Coordinate Attention) to enhance the network's ability to extract features of pineapples in complex backgrounds. Furthermore, the small target detection layer has been fused with BiFPN (Bi-directional Feature Pyramid Network) to strengthen the integration of multi-scale features and enrich the extraction of semantic features. At the same time, the original YOLOv8 detection head has been replaced by the RT-DETR detection head, which incorporates Cross-Attention and Self-Attention mechanisms that improve the model's detection accuracy. Additionally, Focaler-IoU has been employed to improve CIoU, allowing the network to focus more on small targets.Finally, high-resolution images of the pineapple fields were captured using drones to create a dataset, and extensive experiments were conducted. The results indicate that, compared to existing mainstream target detection models, the proposed DPD-YOLO demonstrated superior detection performance for pineapples in situations where the background is complex and the targets are occluded. The

    Keywords: Pineapple detection, UAV, BiFPN, YOLOv8, Coordinate attention

    Received: 06 Nov 2024; Accepted: 06 Jan 2025.

    Copyright: © 2025 Lin, Jiang, Zhao, Xue and Zou. 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:
    Wencheng Jiang, School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, China
    Weiye Zhao, School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, China
    Zhong Xue, South Subtropical Crops Research Institute, Chinese Academy of Tropical Agricultural Sciences, Zhanjiang, 524091, Guangdong Province, China
    Lilan Zou, School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, 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.