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      Lightweight Fruit-Detection Algorithm for Edge Computing Applications

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          Abstract

          In recent years, deep-learning-based fruit-detection technology has exhibited excellent performance in modern horticulture research. However, deploying deep learning algorithms in real-time field applications is still challenging, owing to the relatively low image processing capability of edge devices. Such limitations are becoming a new bottleneck and hindering the utilization of AI algorithms in modern horticulture. In this paper, we propose a lightweight fruit-detection algorithm, specifically designed for edge devices. The algorithm is based on Light-CSPNet as the backbone network, an improved feature-extraction module, a down-sampling method, and a feature-fusion module, and it ensures real-time detection on edge devices while maintaining the fruit-detection accuracy. The proposed algorithm was tested on three edge devices: NVIDIA Jetson Xavier NX, NVIDIA Jetson TX2, and NVIDIA Jetson NANO. The experimental results show that the average detection precision of the proposed algorithm for orange, tomato, and apple datasets are 0.93, 0.847, and 0.850, respectively. Deploying the algorithm, the detection speed of NVIDIA Jetson Xavier NX reaches 21.3, 24.8, and 22.2 FPS, while that of NVIDIA Jetson TX2 reaches 13.9, 14.1, and 14.5 FPS and that of NVIDIA Jetson NANO reaches 6.3, 5.0, and 8.5 FPS for the three datasets. Additionally, the proposed algorithm provides a component add/remove function to flexibly adjust the model structure, considering the trade-off between the detection accuracy and speed in practical usage.

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          Most cited references43

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          You Only Look Once: Unified, Real-Time Object Detection

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            Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

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              SSD: Single Shot MultiBox Detector

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                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                13 October 2021
                2021
                : 12
                : 740936
                Affiliations
                [1] 1Department of Information, Beijing University of Technology , Beijing, China
                [2] 2Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences , Beijing, China
                [3] 3Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture , Beijing, China
                [4] 4International Field Phenomics Research Laboratory, Institute for Sustainable Agro-Ecosystem Services, The University of Tokyo , Tokyo, Japan
                Author notes

                Edited by: Rengarajan Amirtharajan, SASTRA Deemed University, India

                Reviewed by: Thiago Teixeira Santos, Brazilian Agricultural Research Corporation (EMBRAPA), Brazil; Marcin Wozniak, Silesian University of Technology, Poland

                *Correspondence: Wenli Zhang, zhangwenli@ 123456bjut.edu.cn

                This article was submitted to Technical Advances in Plant Science, a section of the journal Frontiers in Plant Science

                Article
                10.3389/fpls.2021.740936
                8548576
                34721466
                601beab9-7437-4a88-a429-de998b214d2c
                Copyright © 2021 Zhang, Liu, Chen, Li, Duan, Wu, Shi and Guo.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 13 July 2021
                : 08 September 2021
                Page count
                Figures: 11, Tables: 7, Equations: 6, References: 43, Pages: 16, Words: 10976
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                modern horticulture,deep learning,fruit detection,lightweight,edge devices
                Plant science & Botany
                modern horticulture, deep learning, fruit detection, lightweight, edge devices

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