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MYOLO: A Lightweight Fresh Shiitake Mushroom Detection Model Based on YOLOv3

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  • Peichao Cong

    (School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China)

  • Hao Feng

    (School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China)

  • Kunfeng Lv

    (School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China)

  • Jiachao Zhou

    (School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China)

  • Shanda Li

    (School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China)

Abstract

Fruit and vegetable inspection aids robotic harvesting in modern agricultural production. For rapid and accurate detection of fresh shiitake mushrooms, picking robots must overcome the complex conditions of the growing environment, diverse morphology, dense shading, and changing field of view. The current work focuses on improving inspection accuracy at the expense of timeliness. This paper proposes a lightweight shiitake mushroom detection model called Mushroom You Only Look Once (MYOLO) based on You Only Look Once (YOLO) v3. To reduce the complexity of the network structure and computation and improve real-time detection, a lightweight GhostNet16 was built instead of DarkNet53 as the backbone network. Spatial pyramid pooling was introduced at the end of the backbone network to achieve multiscale local feature fusion and improve the detection accuracy. Furthermore, a neck network called shuffle adaptive spatial feature pyramid network (ASA-FPN) was designed to improve fresh shiitake mushroom detection, including that of densely shaded mushrooms, as well as the localization accuracy. Finally, the Complete Intersection over Union (CIoU) loss function was used to optimize the model and improve its convergence efficiency. MYOLO achieved a mean average precision ( mAP ) of 97.03%, 29.8M parameters, and a detection speed of 19.78 ms, showing excellent timeliness and detectability with a 2.04% higher mAP and 2.08 times fewer parameters than the original model. Thus, it provides an important theoretical basis for automatic picking of fresh shiitake mushrooms.

Suggested Citation

  • Peichao Cong & Hao Feng & Kunfeng Lv & Jiachao Zhou & Shanda Li, 2023. "MYOLO: A Lightweight Fresh Shiitake Mushroom Detection Model Based on YOLOv3," Agriculture, MDPI, vol. 13(2), pages 1-23, February.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:2:p:392-:d:1060784
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    References listed on IDEAS

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    1. Lifa Fang & Yanqiang Wu & Yuhua Li & Hongen Guo & Hua Zhang & Xiaoyu Wang & Rui Xi & Jialin Hou, 2021. "Using Channel and Network Layer Pruning Based on Deep Learning for Real-Time Detection of Ginger Images," Agriculture, MDPI, vol. 11(12), pages 1-18, November.
    2. Ning Wang & Tingting Qian & Juan Yang & Linyi Li & Yingyu Zhang & Xiuguo Zheng & Yeying Xu & Hanqing Zhao & Jingyin Zhao, 2022. "An Enhanced YOLOv5 Model for Greenhouse Cucumber Fruit Recognition Based on Color Space Features," Agriculture, MDPI, vol. 12(10), pages 1-15, September.
    3. Mohd Asyraf Zulkifley & Asraf Mohamed Moubark & Adhi Harmoko Saputro & Siti Raihanah Abdani, 2022. "Automated Apple Recognition System Using Semantic Segmentation Networks with Group and Shuffle Operators," Agriculture, MDPI, vol. 12(6), pages 1-15, May.
    4. Rong Xiang & Maochen Zhang & Jielan Zhang, 2022. "Recognition for Stems of Tomato Plants at Night Based on a Hybrid Joint Neural Network," Agriculture, MDPI, vol. 12(6), pages 1-21, May.
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    Cited by:

    1. Jinkai Guo & Xiao Xiao & Jianchi Miao & Bingquan Tian & Jing Zhao & Yubin Lan, 2023. "Design and Experiment of a Visual Detection System for Zanthoxylum-Harvesting Robot Based on Improved YOLOv5 Model," Agriculture, MDPI, vol. 13(4), pages 1-18, March.

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