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Real-Time Detection of Vine Trunk for Robot Localization Using Deep Learning Models Developed for Edge TPU Devices

Author

Listed:
  • Khadijeh Alibabaei

    (Department of Electromechanical Engineering, University of Beira Interior, 6201-001 Covilhã, Portugal
    Centre for Mechanical and Aerospace Science and Technologies (C-MAST), University of Beira Interior, 6201-001 Covilhã, Portugal)

  • Eduardo Assunção

    (Department of Electromechanical Engineering, University of Beira Interior, 6201-001 Covilhã, Portugal
    Centre for Mechanical and Aerospace Science and Technologies (C-MAST), University of Beira Interior, 6201-001 Covilhã, Portugal)

  • Pedro D. Gaspar

    (Department of Electromechanical Engineering, University of Beira Interior, 6201-001 Covilhã, Portugal
    Centre for Mechanical and Aerospace Science and Technologies (C-MAST), University of Beira Interior, 6201-001 Covilhã, Portugal)

  • Vasco N. G. J. Soares

    (Polytechnic Institute of Castelo Branco, 6000-084 Castelo Branco, Portugal
    Instituto de Telecomunicações, 6201-001 Covilhã, Portugal)

  • João M. L. P. Caldeira

    (Polytechnic Institute of Castelo Branco, 6000-084 Castelo Branco, Portugal
    Instituto de Telecomunicações, 6201-001 Covilhã, Portugal)

Abstract

The concept of the Internet of Things (IoT) in agriculture is associated with the use of high-tech devices such as robots and sensors that are interconnected to assess or monitor conditions on a particular plot of land and then deploy the various factors of production such as seeds, fertilizer, water, etc., accordingly. Vine trunk detection can help create an accurate map of the vineyard that the agricultural robot can rely on to safely navigate and perform a variety of agricultural tasks such as harvesting, pruning, etc. In this work, the state-of-the-art single-shot multibox detector (SSD) with MobileDet Edge TPU and MobileNet Edge TPU models as the backbone was used to detect the tree trunks in the vineyard. Compared to the SSD with MobileNet-V1, MobileNet-V2, and MobileDet as backbone, the SSD with MobileNet Edge TPU was more accurate in inference on the Raspberrypi, with almost the same inference time on the TPU. The SSD with MobileDet Edge TPU achieved the second-best accurate model. Additionally, this work examines the effects of some features, including the size of the input model, the quantity of training data, and the diversity of the training dataset. Increasing the size of the input model and the training dataset increased the performance of the model.

Suggested Citation

  • Khadijeh Alibabaei & Eduardo Assunção & Pedro D. Gaspar & Vasco N. G. J. Soares & João M. L. P. Caldeira, 2022. "Real-Time Detection of Vine Trunk for Robot Localization Using Deep Learning Models Developed for Edge TPU Devices," Future Internet, MDPI, vol. 14(7), pages 1-16, June.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:7:p:199-:d:852200
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    References listed on IDEAS

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    1. Fotios Zantalis & Grigorios Koulouras & Sotiris Karabetsos & Dionisis Kandris, 2019. "A Review of Machine Learning and IoT in Smart Transportation," Future Internet, MDPI, vol. 11(4), pages 1-23, April.
    2. André Silva Aguiar & Nuno Namora Monteiro & Filipe Neves dos Santos & Eduardo J. Solteiro Pires & Daniel Silva & Armando Jorge Sousa & José Boaventura-Cunha, 2021. "Bringing Semantics to the Vineyard: An Approach on Deep Learning-Based Vine Trunk Detection," Agriculture, MDPI, vol. 11(2), pages 1-20, February.
    3. Luís Loures & Alejandro Chamizo & Paulo Ferreira & Ana Loures & Rui Castanho & Thomas Panagopoulos, 2020. "Assessing the Effectiveness of Precision Agriculture Management Systems in Mediterranean Small Farms," Sustainability, MDPI, vol. 12(9), pages 1-15, May.
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