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Tea Sprout Picking Point Identification Based on Improved DeepLabV3+

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  • Chunyu Yan

    (College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China
    Power Semiconductor Device Reliability Engineering Center of the Ministry of Education, Guiyang 550025, China)

  • Zhonghui Chen

    (College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China)

  • Zhilin Li

    (College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China)

  • Ruixin Liu

    (College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China)

  • Yuxin Li

    (College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China
    Power Semiconductor Device Reliability Engineering Center of the Ministry of Education, Guiyang 550025, China)

  • Hui Xiao

    (College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China)

  • Ping Lu

    (State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, China)

  • Benliang Xie

    (College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China
    Power Semiconductor Device Reliability Engineering Center of the Ministry of Education, Guiyang 550025, China)

Abstract

Tea sprout segmentation and picking point localization via machine vision are the core technologies of automatic tea picking. This study proposes a method of tea segmentation and picking point location based on a lightweight convolutional neural network named MC-DM (Multi-Class DeepLabV3+ MobileNetV2 (Mobile Networks Vision 2)) to solve the problem of tea shoot picking point in a natural environment. In the MC-DM architecture, an optimized MobileNetV2 is used to reduce the number of parameters and calculations. Then, the densely connected atrous spatial pyramid pooling module is introduced into the MC-DM to obtain denser pixel sampling and a larger receptive field. Finally, an image dataset of high-quality tea sprout picking points is established to train and test the MC-DM network. Experimental results show that the MIoU of MC-DM reached 91.85%, which is improved by 8.35% compared with those of several state-of-the-art methods. The optimal improvements of model parameters and detection speed were 89.19% and 16.05 f/s, respectively. After the segmentation results of the MC-DM were applied to the picking point identification, the accuracy of picking point identification reached 82.52%, 90.07%, and 84.78% for single bud, one bud with one leaf, and one bud with two leaves, respectively. This research provides a theoretical reference for fast segmentation and visual localization of automatically picked tea sprouts.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:10:p:1594-:d:931927
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    References listed on IDEAS

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    1. Yingpeng Zhu & Chuanyu Wu & Junhua Tong & Jianneng Chen & Leiying He & Rongyang Wang & Jiangming Jia, 2021. "Deviation Tolerance Performance Evaluation and Experiment of Picking End Effector for Famous Tea," Agriculture, MDPI, vol. 11(2), pages 1-18, February.
    2. Chen Liu & Chunjiang Zhao & Huarui Wu & Xiao Han & Shuqin Li, 2022. "ADDLight: An Energy-Saving Adder Neural Network for Cucumber Disease Classification," Agriculture, MDPI, vol. 12(4), pages 1-17, March.
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    Cited by:

    1. Dimitre D. Dimitrov, 2023. "Internet and Computers for Agriculture," Agriculture, MDPI, vol. 13(1), pages 1-7, January.
    2. 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.

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