CoChat
Sign Up
Cited by 243Smart Agriculture and AIAdvanced Neural Network ApplicationsIndustrial Vision Systems and Defect Detection

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

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

Open access
Kang, Hanwen, Chen, Chao·2020·Computers and Electronics in Agriculture
DOI 10.1016/j.compag.2019.105108
Open this paper in CoChat — free accountOpen PDF

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.

Overview

Integrity signals

Registry

Crossref · 10.1016/j.compag.2019.105108

Cites

6 of 6 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Research works printed but not linked: 29

  1. Bargoti, Suchet, Underwood, James, 2017a. Deep fruit detection in orchards. In: 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 3626–3633. · DOI · Paper page
  2. Bargoti, 2017, Image segmentation for fruit detection and yield estimation in apple orchards, J. Field Robot., 34, 1039, 10.1002/rob.21699 · DOI
  3. Greenspan, 2001, Mixture model for face-color modeling and segmentation, Pattern Recogn. Lett., 22, 1525, 10.1016/S0167-8655(01)00086-1 · DOI
  4. Hashimoto, 2003, A review on vision-based control of robot manipulators, Adv. Robot., 17, 969, 10.1163/156855303322554382 · DOI
  5. He, Kaiming, Gkioxari, Georgia, Dollár, Piotr, Girshick, Ross, 2017. Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969. · DOI · Paper page
  6. Kamilaris, 2018, Francesc X Prenafeta-Boldu. Deep learning in agriculture: a survey, Comput. Electron. Agric., 147, 70, 10.1016/j.compag.2018.02.016 · DOI
  7. Kapach, 2012, Computer vision for fruit harvesting robots–state of the art and challenges ahead, Int. J. Comput. Vision Robot., 3, 4, 10.1504/IJCVR.2012.046419 · DOI
  8. Koirala, 2019, Deep learning for real-time fruit detection and orchard fruit load estimation: benchmarking of ‘mangoyolo’, Precis. Agric., 1
  9. Kussul, 2017, Deep learning classification of land cover and crop types using remote sensing data, IEEE Geosci. Remote Sens. Lett., 14, 778, 10.1109/LGRS.2017.2681128 · DOI
  10. Li, 2017, Deepcotton: in-field cotton segmentation using deep fully convolutional network, J. Electron. Imaging, 26, 053028, 10.1117/1.JEI.26.5.053028 · DOI
  11. Lin, 2019, Guava detection and pose estimation using a low-cost rgb-d sensor in the field, Sensors, 19, 428, 10.3390/s19020428 · DOI
  12. Hao, 2016, Region-based colour modelling for joint crop and maize tassel segmentation, Biosyst. Eng., 147, 139, 10.1016/j.biosystemseng.2016.04.007 · DOI
  13. Majeed, 2018, Apple tree trunk and branch segmentation for automatic trellis training using convolutional neural network based semantic segmentation, IFAC-PapersOnLine, 51, 75, 10.1016/j.ifacol.2018.08.064 · DOI
  14. Ng, 2003, Sift: Predicting amino acid changes that affect protein function, Nucl. Acids Res., 31, 3812, 10.1093/nar/gkg509 · DOI
  15. Pass, Greg, Zabih, Ramin, Miller, Justin, 1996. Comparing images using color coherence vectors. In: ACM Multimedia, vol. 96. Citeseer, pp. 65–73. · DOI
  16. Redmon, Joseph, Farhadi, Ali, 2017. Yolo9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271. · DOI · Paper page
  17. Redmon, Joseph, Farhadi, Ali, 2018. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767. · arXiv
  18. Ren, Shaoqing, He, Kaiming, Girshick, Ross, Sun, Jian, 2015. Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pages 91–99, 2015.
  19. Sa, 2016, Deepfruits: A fruit detection system using deep neural networks, Sensors, 16, 1222, 10.3390/s16081222 · DOI
  20. Srivastava,Rupesh K., Greff, Klaus, Schmidhuber, Jürgen, 2015. Training very deep networks. In: Advances in Neural Information Processing Systems, pp. 2377–2385.
  21. Tian, 2019, Apple detection during different growth stages in orchards using the improved yolo-v3 model, Comput. Electron. Agric., 157, 417, 10.1016/j.compag.2019.01.012 · DOI · Paper page
  22. Uijlings, 2013, Selective search for object recognition, Int. J. Comput. Vision, 104, 154, 10.1007/s11263-013-0620-5 · DOI · Paper page
  23. Wang, Panqu, Chen, Pengfei, Yuan, Ye, Liu, Ding, Huang, Zehua, Hou, Xiaodi, Cottrell, Garrison, 2018. Understanding convolution for semantic segmentation. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, pp. 1451–1460. · DOI
  24. Weiss, 2016, A survey of transfer learning, J. Big Data, 3, 9, 10.1186/s40537-016-0043-6 · DOI · Paper page
  25. Yang, 2019, Deep convolutional neural networks for rice grain yield estimation at the ripening stage using uav-based remotely sensed images, Field Crops Res., 235, 142, 10.1016/j.fcr.2019.02.022 · DOI
  26. Yoo, 2009, Fast normalized cross-correlation, Circuits Syst. Signal Process., 28, 819, 10.1007/s00034-009-9130-7 · DOI
  27. Yang, 2019, Fruit detection for strawberry harvesting robot in non-structural environment based on mask-rcnn, Comput. Electron. Agric., 163, 104846, 10.1016/j.compag.2019.06.001 · DOI
  28. Zhao, 2016, A review of key techniques of vision-based control for harvesting robot, Comput. Electron. Agric., 127, 311, 10.1016/j.compag.2016.06.022 · DOI
  29. Zhong, 2018, A vision-based counting and recognition system for flying insects in intelligent agriculture, Sensors, 18, 1489, 10.3390/s18051489 · DOI

Cited by

Sorted by citations

See all 243

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review