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Improved Apple Fruit Target Recognition Method Based on YOLOv7 Model

Author

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  • Huawei Yang

    (College of Mechanical and Electrical Engineering, Shandong Agricultural University, Tai’an 271002, China
    College of Radiology, Shandong First Medical University, Tai’an 271000, China
    Shandong Academy of Agricultural Machinery Sciences, Jinan 250010, China)

  • Yinzeng Liu

    (Shandong Academy of Agricultural Machinery Sciences, Jinan 250010, China)

  • Shaowei Wang

    (Shandong Academy of Agricultural Machinery Sciences, Jinan 250010, China)

  • Huixing Qu

    (College of Mechanical and Electrical Engineering, Shandong Agricultural University, Tai’an 271002, China)

  • Ning Li

    (Shandong Academy of Agricultural Machinery Sciences, Jinan 250010, China)

  • Jie Wu

    (College of Mechanical and Electrical Engineering, Shandong Agricultural University, Tai’an 271002, China)

  • Yinfa Yan

    (College of Mechanical and Electrical Engineering, Shandong Agricultural University, Tai’an 271002, China)

  • Hongjian Zhang

    (College of Mechanical and Electrical Engineering, Shandong Agricultural University, Tai’an 271002, China)

  • Jinxing Wang

    (College of Mechanical and Electrical Engineering, Shandong Agricultural University, Tai’an 271002, China)

  • Jianfeng Qiu

    (College of Radiology, Shandong First Medical University, Tai’an 271000, China)

Abstract

This study proposes an improved algorithm based on the You Only Look Once v7 (YOLOv7) to address the low accuracy of apple fruit target recognition caused by high fruit density, occlusion, and overlapping issues. Firstly, we proposed a preprocessing algorithm for the split image with overlapping to improve the robotic intelligent picking recognition accuracy. Then, we divided the training, validation, and test sets. Secondly, the MobileOne module was introduced into the backbone network of YOLOv7 to achieve parametric fusion and reduce network computation. Afterward, we improved the SPPCSPS module and changed the serial channel to the parallel channel to enhance the speed of image feature fusion. We added an auxiliary detection head to the head structure. Finally, we conducted fruit target recognition based on model validation and tests. The results showed that the accuracy of the improved YOLOv7 algorithm increased by 6.9%. The recall rate increased by 10%, the mAP1 algorithm increased by 5%, and the mAP2 algorithm increased by 3.8%. The accuracy of the improved YOLOv7 algorithm was 3.5%, 14%, 9.1%, and 6.5% higher than that of other control YOLO algorithms, verifying that the improved YOLOv7 algorithm could significantly improve the fruit target recognition in high-density fruits.

Suggested Citation

  • Huawei Yang & Yinzeng Liu & Shaowei Wang & Huixing Qu & Ning Li & Jie Wu & Yinfa Yan & Hongjian Zhang & Jinxing Wang & Jianfeng Qiu, 2023. "Improved Apple Fruit Target Recognition Method Based on YOLOv7 Model," Agriculture, MDPI, vol. 13(7), pages 1-21, June.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:7:p:1278-:d:1175882
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    References listed on IDEAS

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    1. Takuya Otani & Akira Itoh & Hideki Mizukami & Masatsugu Murakami & Shunya Yoshida & Kota Terae & Taiga Tanaka & Koki Masaya & Shuntaro Aotake & Masatoshi Funabashi & Atsuo Takanishi, 2022. "Agricultural Robot under Solar Panels for Sowing, Pruning, and Harvesting in a Synecoculture Environment," Agriculture, MDPI, vol. 13(1), pages 1-22, December.
    2. Pan Fan & Guodong Lang & Pengju Guo & Zhijie Liu & Fuzeng Yang & Bin Yan & Xiaoyan Lei, 2021. "Multi-Feature Patch-Based Segmentation Technique in the Gray-Centered RGB Color Space for Improved Apple Target Recognition," Agriculture, MDPI, vol. 11(3), pages 1-18, March.
    3. Eleni Vrochidou & Viktoria Nikoleta Tsakalidou & Ioannis Kalathas & Theodoros Gkrimpizis & Theodore Pachidis & Vassilis G. Kaburlasos, 2022. "An Overview of End Effectors in Agricultural Robotic Harvesting Systems," Agriculture, MDPI, vol. 12(8), pages 1-35, August.
    4. 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.
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    Cited by:

    1. Rihong Zhang & Zejun Huang & Yuling Zhang & Zhong Xue & Xiaomin Li, 2023. "MSGV-YOLOv7: A Lightweight Pineapple Detection Method," Agriculture, MDPI, vol. 14(1), pages 1-16, December.
    2. Kunpeng Zhao & Jinyang Li & Wenqiang Shi & Liqiang Qi & Chuntao Yu & Wei Zhang, 2024. "Field-Based Soybean Flower and Pod Detection Using an Improved YOLOv8-VEW Method," Agriculture, MDPI, vol. 14(8), pages 1-15, August.
    3. Ping Dong & Kuo Li & Ming Wang & Feitao Li & Wei Guo & Haiping Si, 2023. "Maize Leaf Compound Disease Recognition Based on Attention Mechanism," Agriculture, MDPI, vol. 14(1), pages 1-22, December.
    4. Feng Xiao & Haibin Wang & Yueqin Xu & Zhen Shi, 2023. "A Lightweight Detection Method for Blueberry Fruit Maturity Based on an Improved YOLOv5 Algorithm," Agriculture, MDPI, vol. 14(1), pages 1-18, December.
    5. Mingming Liu & Yinzeng Liu & Qihuan Wang & Qinghao He & Duanyang Geng, 2024. "Real-Time Detection Technology of Corn Kernel Breakage and Mildew Based on Improved YOLOv5s," Agriculture, MDPI, vol. 14(5), pages 1-16, May.

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