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Fast implementation of real-time fruit detection in apple orchards using deep learning

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Kang, Hanwen, Chen, Chao·2020·Computers and Electronics in Agriculture
DOI 10.1016/j.compag.2019.105108
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Abstract

To perform robust and efficient fruit detection in orchards is challenging since there are a number of variances in the working environments. Recently, deep-learning have shown a promising performance in many visual-guided agriculture applications. However, deep-learning based approaches requires labelling on training data, which is a labour-intensive and time-consuming task. In this study, a fast implementation framework of a deep-learning based fruit detector for apple harvesting is developed. The developed framework comprises an auto label generation module and a deep-learning-based fruit detector ‘LedNet’. The Label Generation algorithm utilises the multi-scale pyramid and clustering classifier to assist fast labelling of training data. LedNet adopts feature pyramid network and atrous spatial pyramid pooling to improve the detection performance of the model. A light-weight backbone is also developed and utilised to improve computational efficiency. From the experimental results, LedNet achieves 0.821 and 0.853 on recall and accuracy on apple detection in orchards, and its weights size and inference time are 7.4 M and 28 ms, respectively. The experimental results show that LedNet can perform real-time apple detection in orchards robustly and efficiently.

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Metadata

Authors
Kang, H., & Chen, C.
Affiliations
Monash University (AU)
Corresponding author
Chen, Chao
ORCID
Venue
Computers and Electronics in Agriculture
Publisher
Elsevier BV
Published
2020-01
Volume
168
Pages
105108
ISSN
0168-1699 (ISSN-L)
DOI
10.1016/j.compag.2019.105108
OpenAlex
W2989928569
Cited by
243
References
6
Citations by year
2026: 20 · 2025: 23 · 2024: 41 · 2023: 48 · 2022: 53 · 2021: 39 · 2020: 19
FWCI
20.47
Type
journal article
Language
en
Open access
Licence
Locations
  • Computers and Electronics in Agriculture (publishedVersion)
  • Monash University Research Portal (Monash University) (submittedVersion, cc-by-nc-nd)
Funders
Australian Research Council
Topics
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
Keywords
apple detection, fruit detection, apple orchards, real-time detection, feature pyramid network, atrous spatial pyramid pooling, lightweight backbone, apple picking

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