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A Counting Method of Red Jujube Based on Improved YOLOv5s

Author

Listed:
  • Yichen Qiao

    (College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China)

  • Yaohua Hu

    (College of Optical, Mechanical, and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China)

  • Zhouzhou Zheng

    (College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China)

  • Huanbo Yang

    (College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China)

  • Kaili Zhang

    (College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China)

  • Juncai Hou

    (College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China)

  • Jiapan Guo

    (Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, 9747 AG Groningen, The Netherlands
    Data Science Center in Health (DASH), University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands)

Abstract

Due to complex environmental factors such as illumination, shading between leaves and fruits, shading between fruits, and so on, it is a challenging task to quickly identify red jujubes and count red jujubes in orchards. A counting method of red jujube based on improved YOLOv5s was proposed, which realized the fast and accurate detection of red jujubes and reduced the model scale and estimation error. ShuffleNet V2 was used as the backbone of the model to improve model detection ability and light the weight. In addition, the Stem, a novel data loading module, was proposed to prevent the loss of information due to the change in feature map size. PANet was replaced by BiFPN to enhance the model feature fusion capability and improve the model accuracy. Finally, the improved YOLOv5s detection model was used to count red jujubes. The experimental results showed that the overall performance of the improved model was better than that of YOLOv5s. Compared with the YOLOv5s, the improved model was 6.25% and 8.33% of the original network in terms of the number of model parameters and model size, and the Precision, Recall, F1-score, AP, and Fps were improved by 4.3%, 2.0%, 3.1%, 0.6%, and 3.6%, respectively. In addition, RMSE and MAPE decreased by 20.87% and 5.18%, respectively. Therefore, the improved model has advantages in memory occupation and recognition accuracy, and the method provides a basis for the estimation of red jujube yield by vision.

Suggested Citation

  • Yichen Qiao & Yaohua Hu & Zhouzhou Zheng & Huanbo Yang & Kaili Zhang & Juncai Hou & Jiapan Guo, 2022. "A Counting Method of Red Jujube Based on Improved YOLOv5s," Agriculture, MDPI, vol. 12(12), pages 1-20, December.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:12:p:2071-:d:991377
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    References listed on IDEAS

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    1. Damar Novtahaning & Hasnain Ali Shah & Jae-Mo Kang, 2022. "Deep Learning Ensemble-Based Automated and High-Performing Recognition of Coffee Leaf Disease," Agriculture, MDPI, vol. 12(11), pages 1-16, November.
    2. Xiaoyu Li & Yuefeng Du & Lin Yao & Jun Wu & Lei Liu, 2021. "Design and Experiment of a Broken Corn Kernel Detection Device Based on the Yolov4-Tiny Algorithm," Agriculture, MDPI, vol. 11(12), pages 1-17, December.
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    Cited by:

    1. Vadim Bolshev & Vladimir Panchenko & Alexey Sibirev, 2023. "Engineering Innovations in Agriculture," Agriculture, MDPI, vol. 13(7), pages 1-4, June.

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