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Research on Apple Detection and Tracking Count in Complex Scenes Based on the Improved YOLOv7-Tiny-PDE

Author

Listed:
  • Dongxuan Cao

    (North China Institute of Aerospace Engineering, Langfang 065000, China
    These authors contributed equally to this work.)

  • Wei Luo

    (North China Institute of Aerospace Engineering, Langfang 065000, China
    Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China
    National Joint Engineering Research Center of Space Remote Sensing Information Application Technology, Langfang 065000, China
    These authors contributed equally to this work.)

  • Ruiyin Tang

    (North China Institute of Aerospace Engineering, Langfang 065000, China
    Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China
    National Joint Engineering Research Center of Space Remote Sensing Information Application Technology, Langfang 065000, China)

  • Yuyan Liu

    (North China Institute of Aerospace Engineering, Langfang 065000, China
    Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China
    National Joint Engineering Research Center of Space Remote Sensing Information Application Technology, Langfang 065000, China)

  • Jiasen Zhao

    (North China Institute of Aerospace Engineering, Langfang 065000, China)

  • Xuqing Li

    (North China Institute of Aerospace Engineering, Langfang 065000, China
    Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China
    National Joint Engineering Research Center of Space Remote Sensing Information Application Technology, Langfang 065000, China)

  • Lihua Yuan

    (North China Institute of Aerospace Engineering, Langfang 065000, China
    Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China
    National Joint Engineering Research Center of Space Remote Sensing Information Application Technology, Langfang 065000, China)

Abstract

Accurately detecting apple fruit can crucially assist in estimating the fruit yield in apple orchards in complex scenarios. In such environments, the factors of density, leaf occlusion, and fruit overlap can affect the detection and counting accuracy. This paper proposes an improved YOLOv7-Tiny-PDE network model based on the YOLOv7-Tiny model to detect and count apples from data collected by drones, considering various occlusion and lighting conditions. First, within the backbone network, we replaced the simplified efficient layer aggregation network (ELAN) with partial convolution (PConv), reducing the network parameters and computational redundancy while maintaining the detection accuracy. Second, in the neck network, we used a dynamic detection head to replace the original detection head, effectively suppressing the background interference and capturing the background information more comprehensively, thus enhancing the detection accuracy for occluded targets and improving the fruit feature extraction. To further optimize the model, we replaced the boundary box loss function from CIOU to EIOU. For fruit counting across video frames in complex occlusion scenes, we integrated the improved model with the DeepSort tracking algorithm based on Kalman filtering and motion trajectory prediction with a cascading matching algorithm. According to experimental results, compared with the baseline YOLOv7-Tiny, the improved model reduced the total parameters by 22.2% and computation complexity by 18.3%. Additionally, in data testing, the p -value improved by 0.5%; the R-value rose by 2.7%; the mAP and F1 scores rose by 4% and 1.7%, respectively; and the MOTA value improved by 2%. The improved model is more lightweight and can preserve a high detection accuracy well, and hence, it can be applied to detection and counting tasks in complex orchards and provides a new solution for fruit yield estimation using lightweight devices.

Suggested Citation

  • Dongxuan Cao & Wei Luo & Ruiyin Tang & Yuyan Liu & Jiasen Zhao & Xuqing Li & Lihua Yuan, 2025. "Research on Apple Detection and Tracking Count in Complex Scenes Based on the Improved YOLOv7-Tiny-PDE," Agriculture, MDPI, vol. 15(5), pages 1-26, February.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:5:p:483-:d:1598248
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    References listed on IDEAS

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    1. Wei Ji & Yu Pan & Bo Xu & Juncheng Wang, 2022. "A Real-Time Apple Targets Detection Method for Picking Robot Based on ShufflenetV2-YOLOX," Agriculture, MDPI, vol. 12(6), pages 1-18, June.
    2. H. W. Kuhn, 1955. "The Hungarian method for the assignment problem," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 2(1‐2), pages 83-97, March.
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