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Real-Time Image Detection for Edge Devices: A Peach Fruit Detection Application

Author

Listed:
  • Eduardo Assunção

    (C-MAST Center for Mechanical and Aerospace Science and Technologies, University of Beira Interior, 6201-001 Covilhã, Portugal
    Instituto de Telecomunicações, University of Beira Interior, Rua Marquês d’Ávila e Bolama, 6201-001 Covilhã, Portugal)

  • Pedro D. Gaspar

    (C-MAST Center for Mechanical and Aerospace Science and Technologies, University of Beira Interior, 6201-001 Covilhã, Portugal
    Department of Electromechanical Engineering, University of Beira Interior, Rua Marquês d’Ávila e Bolama, 6201-001 Covilhã, Portugal)

  • Khadijeh Alibabaei

    (C-MAST Center for Mechanical and Aerospace Science and Technologies, University of Beira Interior, 6201-001 Covilhã, Portugal
    Department of Electromechanical Engineering, University of Beira Interior, Rua Marquês d’Ávila e Bolama, 6201-001 Covilhã, Portugal)

  • Maria P. Simões

    (Polytechnic Institute of Castelo Branco, Av. Pedro Álvares Cabral nº 12, 6000-084 Castelo Branco, Portugal
    CERNAS, Research Center for Natural Resources, Environment and Society, Escola Superiora Agrária de Coimbra Bencanta, 3045-601 Coimbra, Portugal)

  • Hugo Proença

    (Instituto de Telecomunicações, University of Beira Interior, Rua Marquês d’Ávila e Bolama, 6201-001 Covilhã, Portugal)

  • Vasco N. G. J. Soares

    (Instituto de Telecomunicações, University of Beira Interior, Rua Marquês d’Ávila e Bolama, 6201-001 Covilhã, Portugal
    Polytechnic Institute of Castelo Branco, Av. Pedro Álvares Cabral nº 12, 6000-084 Castelo Branco, Portugal)

  • João M. L. P. Caldeira

    (Instituto de Telecomunicações, University of Beira Interior, Rua Marquês d’Ávila e Bolama, 6201-001 Covilhã, Portugal
    Polytechnic Institute of Castelo Branco, Av. Pedro Álvares Cabral nº 12, 6000-084 Castelo Branco, Portugal)

Abstract

Within the scope of precision agriculture, many applications have been developed to support decision making and yield enhancement. Fruit detection has attracted considerable attention from researchers, and it can be used offline. In contrast, some applications, such as robot vision in orchards, require computer vision models to run on edge devices while performing inferences at high speed. In this area, most modern applications use an integrated graphics processing unit (GPU). In this work, we propose the use of a tensor processing unit (TPU) accelerator with a Raspberry Pi target device and the state-of-the-art, lightweight, and hardware-aware MobileDet detector model. Our contribution is the extension of the possibilities of using accelerators (the TPU) for edge devices in precision agriculture. The proposed method was evaluated using a novel dataset of peaches with three cultivars, which will be made available for further studies. The model achieved an average precision (AP) of 88.2% and a performance of 19.84 frames per second (FPS) at an image size of 640 × 480. The results obtained show that the TPU accelerator can be an excellent alternative for processing on the edge in precision agriculture.

Suggested Citation

  • Eduardo Assunção & Pedro D. Gaspar & Khadijeh Alibabaei & Maria P. Simões & Hugo Proença & Vasco N. G. J. Soares & João M. L. P. Caldeira, 2022. "Real-Time Image Detection for Edge Devices: A Peach Fruit Detection Application," Future Internet, MDPI, vol. 14(11), pages 1-12, November.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:11:p:323-:d:966399
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    References listed on IDEAS

    as
    1. Khadijeh Alibabaei & Pedro D. Gaspar & Tânia M. Lima, 2021. "Crop Yield Estimation Using Deep Learning Based on Climate Big Data and Irrigation Scheduling," Energies, MDPI, vol. 14(11), pages 1-21, May.
    2. Alibabaei, Khadijeh & Gaspar, Pedro D. & Assunção, Eduardo & Alirezazadeh, Saeid & Lima, Tânia M., 2022. "Irrigation optimization with a deep reinforcement learning model: Case study on a site in Portugal," Agricultural Water Management, Elsevier, vol. 263(C).
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