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Grape Cluster Real-Time Detection in Complex Natural Scenes Based on YOLOv5s Deep Learning Network

Author

Listed:
  • Chuandong Zhang

    (School of Mathematics and Computer Application Technology, Jining University, Qufu 273100, China
    College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China)

  • Huali Ding

    (School of Mathematics and Computer Application Technology, Jining University, Qufu 273100, China)

  • Qinfeng Shi

    (School of Mathematics and Computer Application Technology, Jining University, Qufu 273100, China)

  • Yunfei Wang

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

Abstract

Due to differences in planting environment, color, shape, size, and compactness, accurate detection of grape clusters is very difficult. Herein, a real-time detection method for grape clusters based on the YOLOv5s deep learning algorithm was proposed. More specifically, a novel dataset called Grape-internet was constructed, which consisted of 8657 grape images and corresponding annotation files in complex scenes. By training and adjusting the parameters of the YOLOv5s model on the data set, and by reducing the depth and width of the network, the lightweight processing of the network was completed, losing only a small amount of accuracy. As a result, the fast and accurate detection of grape clusters was finally realized. The test results showed that the precision, recall, mAP and F1 of the grape cluster detection network were 99.40%, 99.40%, 99.40% and 99.40%, respectively, and the average detection speed per image was 344.83 fps, with a model size of 13.67 MB. Compared with the YOLOv5x, ScaledYOLOv4-CSP and YOLOv3 models, the precision of YOLOv5s was 1.84% higher than that of ScaledYOLOv4-CSP, and the recall rate and mAP were slightly lower than three networks by 0.1–0.3%. The speed was the fastest (4.6 times, 2.83 times and 6.7 times of YOLOv3, ScaledYOLOv4-CSP and YOLOv5x network, respectively) and the network scale was the smallest (1.61%, 6.81% and 8.28% of YOLOv3, ScaledYOLOv4-CSP YOLOv5x, respectively) for YOLOv5s. Moreover, the detection precision and recall rate of YOLOv5s was 26.14% and 30.96% higher, respectively, than those of Mask R-CNN. Further, it exhibited more lightweight and better real-time performance. In short, the detection network can not only meet the requirements of being a high precision, high speed and lightweight solution for grape cluster detection, but also it can adapt to differences between products and complex environmental interference, possessing strong robustness, generalization, and real-time adaptability.

Suggested Citation

  • Chuandong Zhang & Huali Ding & Qinfeng Shi & Yunfei Wang, 2022. "Grape Cluster Real-Time Detection in Complex Natural Scenes Based on YOLOv5s Deep Learning Network," Agriculture, MDPI, vol. 12(8), pages 1-12, August.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:8:p:1242-:d:890546
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    Citations

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    Cited by:

    1. Anwen Liu & Yang Xiang & Yajun Li & Zhengfang Hu & Xiufeng Dai & Xiangming Lei & Zhenhui Tang, 2022. "3D Positioning Method for Pineapple Eyes Based on Multiangle Image Stereo-Matching," Agriculture, MDPI, vol. 12(12), pages 1-17, November.
    2. Zejin Sun & Hui Yang & Zhifu Zhang & Junxiao Liu & Xirui Zhang, 2022. "An Improved YOLOv5-Based Tapping Trajectory Detection Method for Natural Rubber Trees," Agriculture, MDPI, vol. 12(9), pages 1-19, August.
    3. Xingmei Xu & Lu Wang & Xuewen Liang & Lei Zhou & Youjia Chen & Puyu Feng & Helong Yu & Yuntao Ma, 2023. "Maize Seedling Leave Counting Based on Semi-Supervised Learning and UAV RGB Images," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    4. Anlan Ding & Baoliang Peng & Ke Yang & Yanhua Zhang & Xiaoxuan Yang & Xiuguo Zou & Zhangqing Zhu, 2022. "Design of a Machine Vision-Based Automatic Digging Depth Control System for Garlic Combine Harvester," Agriculture, MDPI, vol. 12(12), pages 1-19, December.

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