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

Eduardo Assunção, Pedro D. Gaspar (), Khadijeh Alibabaei, Maria P. Simões, Hugo Proença, Vasco N. G. J. Soares and João M. L. P. Caldeira
Additional contact information
Eduardo Assunção: C-MAST Center for Mechanical and Aerospace Science and Technologies, University of Beira Interior, 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
Khadijeh Alibabaei: C-MAST Center for Mechanical and Aerospace Science and Technologies, University of Beira Interior, 6201-001 Covilhã, Portugal
Maria P. Simões: Polytechnic Institute of Castelo Branco, Av. Pedro Álvares Cabral nº 12, 6000-084 Castelo Branco, 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
João M. L. P. Caldeira: Instituto de Telecomunicações, University of Beira Interior, Rua Marquês d’Ávila e Bolama, 6201-001 Covilhã, Portugal

Future Internet, 2022, vol. 14, issue 11, 1-12

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.

Keywords: deep learning; edge device; object detection; precision agriculture; TPU accelerator (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/14/11/323/pdf (application/pdf)
https://www.mdpi.com/1999-5903/14/11/323/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:14:y:2022:i:11:p:323-:d:966399

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().