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Crop Node Detection and Internode Length Estimation Using an Improved YOLOv5 Model

Author

Listed:
  • Jinnan Hu

    (Guangxi Key Lab of Multisource Information Mining & Security, College of Computer Science & Engineering, Guangxi Normal University, Guilin 541004, China)

  • Guo Li

    (Guangxi Key Lab of Multisource Information Mining & Security, College of Computer Science & Engineering, Guangxi Normal University, Guilin 541004, China)

  • Haolan Mo

    (Guilin Center for Agricultural Science & Technology Research, Guilin 541004, China)

  • Yibo Lv

    (Guangxi Key Lab of Multisource Information Mining & Security, College of Computer Science & Engineering, Guangxi Normal University, Guilin 541004, China)

  • Tingting Qian

    (Agricultural Information Institutes of Science and Technology, Shanghai Academy of Agriculture Sciences, Shanghai 201403, China)

  • Ming Chen

    (Guangxi Key Lab of Multisource Information Mining & Security, College of Computer Science & Engineering, Guangxi Normal University, Guilin 541004, China)

  • Shenglian Lu

    (Guangxi Key Lab of Multisource Information Mining & Security, College of Computer Science & Engineering, Guangxi Normal University, Guilin 541004, China)

Abstract

The extraction and analysis of plant phenotypic characteristics are critical issues for many precision agriculture applications. An improved YOLOv5 model was proposed in this study for accurate node detection and internode length estimation of crops by using an end-to-end approach. In this improved YOLOv5, a feature extraction module was added in front of each detection head, and the bounding box loss function used in the original network of YOLOv5 was replaced by the SIoU bounding box loss function. The results of the experiments on three different crops (chili, eggplant, and tomato) showed that the improved YOLOv5 reached 90.5% AP (average precision) and the average detection time was 0.019 s per image. The average error of the internode length estimation was 41.3 pixels, and the relative error was 7.36%. Compared with the original YOLOv5, the improved YOLOv5 had an average error reduction of 5.84 pixels and a relative error reduction of 1.61%.

Suggested Citation

  • Jinnan Hu & Guo Li & Haolan Mo & Yibo Lv & Tingting Qian & Ming Chen & Shenglian Lu, 2023. "Crop Node Detection and Internode Length Estimation Using an Improved YOLOv5 Model," Agriculture, MDPI, vol. 13(2), pages 1-17, February.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:2:p:473-:d:1070621
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