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Recognition and Positioning of Fresh Tea Buds Using YOLOv4-lighted + ICBAM Model and RGB-D Sensing

Author

Listed:
  • Shudan Guo

    (Beijing Key Laboratory of Optimization Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University, Beijing 100083, China)

  • Seung-Chul Yoon

    (Quality & Safety Assessment Research Unit, U. S. National Poultry Research Center, USDA-ARS, 950 College Station Rd., Athens, GA 30605, USA)

  • Lei Li

    (Zhanglou Town Government of Chengwu County, Heze 274205, China)

  • Wei Wang

    (Beijing Key Laboratory of Optimization Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University, Beijing 100083, China)

  • Hong Zhuang

    (Quality & Safety Assessment Research Unit, U. S. National Poultry Research Center, USDA-ARS, 950 College Station Rd., Athens, GA 30605, USA)

  • Chaojie Wei

    (Beijing Key Laboratory of Optimization Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University, Beijing 100083, China)

  • Yang Liu

    (Beijing Key Laboratory of Optimization Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University, Beijing 100083, China)

  • Yuwen Li

    (Beijing Key Laboratory of Optimization Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University, Beijing 100083, China)

Abstract

To overcome the low recognition accuracy, slow speed, and difficulty in locating the picking points of tea buds, this paper is concerned with the development of a deep learning method, based on the You Only Look Once Version 4 (YOLOv4) object detection algorithm, for the detection of tea buds and their picking points with tea-picking machines. The segmentation method, based on color and depth data from a stereo vision camera, is proposed to detect the shapes of tea buds in 2D and 3D spaces more accurately than using 2D images. The YOLOv4 deep learning model for object detection was modified to obtain a lightweight model with a shorter inference time, called YOLOv4-lighted. Then, Squeeze-and-Excitation Networks (SENet), Efficient Channel Attention (ECA), Convolutional Block Attention Module (CBAM), and improved CBAM (ICBAM) were added to the output layer of the feature extraction network, for improving the detection accuracy of tea features. Finally, the Path Aggregation Network (PANet) in the neck network was simplified to the Feature Pyramid Network (FPN). The light-weighted YOLOv4 with ICBAM, called YOLOv4-lighted + ICBAM, was determined as the optimal recognition model for the detection of tea buds in terms of accuracy (94.19%), recall (93.50%), F1 score (0.94), and average precision (97.29%). Compared with the baseline YOLOv4 model, the size of the YOLOv4-lighted + ICBAM model decreased by 75.18%, and the frame rate increased by 7.21%. In addition, the method for predicting the picking point of each detected tea bud was developed by segmentation of the tea buds in each detected bounding box, with filtering of each segment based on its depth from the camera. The test results showed that the average positioning success rate and the average positioning time were 87.10% and 0.12 s, respectively. In conclusion, the recognition and positioning method proposed in this paper provides a theoretical basis and method for the automatic picking of tea buds.

Suggested Citation

  • Shudan Guo & Seung-Chul Yoon & Lei Li & Wei Wang & Hong Zhuang & Chaojie Wei & Yang Liu & Yuwen Li, 2023. "Recognition and Positioning of Fresh Tea Buds Using YOLOv4-lighted + ICBAM Model and RGB-D Sensing," Agriculture, MDPI, vol. 13(3), pages 1-19, February.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:3:p:518-:d:1076010
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    References listed on IDEAS

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    1. Chunyu Yan & Zhonghui Chen & Zhilin Li & Ruixin Liu & Yuxin Li & Hui Xiao & Ping Lu & Benliang Xie, 2022. "Tea Sprout Picking Point Identification Based on Improved DeepLabV3+," Agriculture, MDPI, vol. 12(10), pages 1-15, October.
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    Cited by:

    1. Zejun Wang & Shihao Zhang & Lijiao Chen & Wendou Wu & Houqiao Wang & Xiaohui Liu & Zongpei Fan & Baijuan Wang, 2024. "Microscopic Insect Pest Detection in Tea Plantations: Improved YOLOv8 Model Based on Deep Learning," Agriculture, MDPI, vol. 14(10), pages 1-21, October.

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