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Apple Grading Method Design and Implementation for Automatic Grader Based on Improved YOLOv5

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  • Bo Xu

    (School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Xiang Cui

    (School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Wei Ji

    (School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Hao Yuan

    (School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Juncheng Wang

    (School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China)

Abstract

Apple grading is an essential part of the apple marketing process to achieve high profits. In this paper, an improved YOLOv5 apple grading method is proposed to address the problems of low grading accuracy and slow grading speed in the apple grading process and is experimentally verified by the designed automatic apple grading machine. Firstly, the Mish activation function is used instead of the original YOLOv5 activation function, which allows the apple feature information to flow in the deep network and improves the generalization ability of the model. Secondly, the distance intersection overUnion loss function (DIoU_Loss) is used to speed up the border regression rate and improve the model convergence speed. In order to refine the model to focus on apple feature information, a channel attention module (Squeeze Excitation) was added to the YOLOv5 backbone network to enhance information propagation between features and improve the model’s ability to extract fruit features. The experimental results show that the improved YOLOv5 algorithm achieves an average accuracy of 90.6% for apple grading under the test set, which is 14.8%, 11.1%, and 3.7% better than the SSD, YOLOv4, and YOLOv5s models, respectively, with a real-time grading frame rate of 59.63 FPS. Finally, the improved YOLOv5 apple grading algorithm is experimentally validated on the developed apple auto-grader. The improved YOLOv5 apple grading algorithm was experimentally validated on the developed apple auto grader. The experimental results showed that the grading accuracy of the automatic apple grader reached 93%, and the grading speed was four apples/sec, indicating that this method has a high grading speed and accuracy for apples, which is of practical significance for advancing the development of automatic apple grading.

Suggested Citation

  • Bo Xu & Xiang Cui & Wei Ji & Hao Yuan & Juncheng Wang, 2023. "Apple Grading Method Design and Implementation for Automatic Grader Based on Improved YOLOv5," Agriculture, MDPI, vol. 13(1), pages 1-18, January.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:1:p:124-:d:1022817
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    References listed on IDEAS

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    1. Wei Chen & Jingfeng Zhang & Biyu Guo & Qingyu Wei & Zhiyu Zhu, 2021. "An Apple Detection Method Based on Des-YOLO v4 Algorithm for Harvesting Robots in Complex Environment," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-12, October.
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    Cited by:

    1. Hang Zhou & Jin Gao & Fan Zhang & Junxiong Zhang & Song Wang & Chunlong Zhang & Wei Li, 2023. "Evaluation of Cutting Stability of a Natural-Rubber-Tapping Robot," Agriculture, MDPI, vol. 13(3), pages 1-23, February.
    2. Jin Yuan & Wei Ji & Qingchun Feng, 2023. "Robots and Autonomous Machines for Sustainable Agriculture Production," Agriculture, MDPI, vol. 13(7), pages 1-4, July.
    3. Long Su & Ruijia Liu & Kenan Liu & Kai Li & Li Liu & Yinggang Shi, 2023. "Greenhouse Tomato Picking Robot Chassis," Agriculture, MDPI, vol. 13(3), pages 1-23, February.

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