A Deep Object Detection Method for Pineapple Fruit and Flower Recognition in Cluttered Background

Chen Wang; Jun Zhou; Cheng yuan Xu; Xiao Bai

doi:10.1007/978-3-030-59830-3_19

A Deep Object Detection Method for Pineapple Fruit and Flower Recognition in Cluttered Background

Chen Wang, Jun Zhou, Cheng yuan Xu, Xiao Bai

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings › peer-review

3 Citations (Scopus)

Abstract

Natural initiation of pineapple flowers is not synchronized, which yields difficulties in yield prediction and the decision of harvest. Computer vision based pineapple detection system is an automated solution to address this issue. However, it is faced with significant challenges, e.g. pineapple flowers and fruits vary in size at different growing stages, the images are influenced by camera viewpoint, illumination conditions, occlusion and so on. This paper presents an approach for pineapple fruit and flower recognition using a state-of-the-art deep object detection model. We collected images from pineapple orchard using three different cameras and selected suitable ones to create a dataset. The experimental results show promising detection performance, with an mAP of 0.64 and F₁ score of 0.69.

Original language	English
Title of host publication	Pattern Recognition and Artificial Intelligence - International Conference, ICPRAI 2020, Proceedings
Editors	Yue Lu, Nicole Vincent, Pong Chi Yuen, Wei-Shi Zheng, Farida Cheriet, Ching Y. Suen
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	218-227
Number of pages	10
ISBN (Print)	9783030598297
DOIs	https://doi.org/10.1007/978-3-030-59830-3_19
Publication status	Published - 2020
Externally published	Yes
Event	2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020 - Zhongshan, China Duration: 19 Oct 2020 → 23 Oct 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12068 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020
Country/Territory	China
City	Zhongshan
Period	19/10/20 → 23/10/20

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Access to Document

10.1007/978-3-030-59830-3_19

Cite this

Wang, C., Zhou, J., Xu, C. Y., & Bai, X. (2020). A Deep Object Detection Method for Pineapple Fruit and Flower Recognition in Cluttered Background. In Y. Lu, N. Vincent, P. C. Yuen, W.-S. Zheng, F. Cheriet, & C. Y. Suen (Eds.), Pattern Recognition and Artificial Intelligence - International Conference, ICPRAI 2020, Proceedings (pp. 218-227). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12068 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-59830-3_19

Wang, Chen ; Zhou, Jun ; Xu, Cheng yuan et al. / A Deep Object Detection Method for Pineapple Fruit and Flower Recognition in Cluttered Background. Pattern Recognition and Artificial Intelligence - International Conference, ICPRAI 2020, Proceedings. editor / Yue Lu ; Nicole Vincent ; Pong Chi Yuen ; Wei-Shi Zheng ; Farida Cheriet ; Ching Y. Suen. Springer Science and Business Media Deutschland GmbH, 2020. pp. 218-227 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{6a341252e03c40c9b159d9de4c93c20e,

title = "A Deep Object Detection Method for Pineapple Fruit and Flower Recognition in Cluttered Background",

abstract = "Natural initiation of pineapple flowers is not synchronized, which yields difficulties in yield prediction and the decision of harvest. Computer vision based pineapple detection system is an automated solution to address this issue. However, it is faced with significant challenges, e.g. pineapple flowers and fruits vary in size at different growing stages, the images are influenced by camera viewpoint, illumination conditions, occlusion and so on. This paper presents an approach for pineapple fruit and flower recognition using a state-of-the-art deep object detection model. We collected images from pineapple orchard using three different cameras and selected suitable ones to create a dataset. The experimental results show promising detection performance, with an mAP of 0.64 and F1 score of 0.69.",

keywords = "Deep learning, Pineapple detection, YOLOv3",

author = "Chen Wang and Jun Zhou and Xu, {Cheng yuan} and Xiao Bai",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020 ; Conference date: 19-10-2020 Through 23-10-2020",

year = "2020",

doi = "10.1007/978-3-030-59830-3_19",

language = "English",

isbn = "9783030598297",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "218--227",

editor = "Yue Lu and Nicole Vincent and Yuen, {Pong Chi} and Wei-Shi Zheng and Farida Cheriet and Suen, {Ching Y.}",

booktitle = "Pattern Recognition and Artificial Intelligence - International Conference, ICPRAI 2020, Proceedings",

address = "Germany",

}

Wang, C, Zhou, J, Xu, CY & Bai, X 2020, A Deep Object Detection Method for Pineapple Fruit and Flower Recognition in Cluttered Background. in Y Lu, N Vincent, PC Yuen, W-S Zheng, F Cheriet & CY Suen (eds), Pattern Recognition and Artificial Intelligence - International Conference, ICPRAI 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12068 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 218-227, 2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020, Zhongshan, China, 19/10/20. https://doi.org/10.1007/978-3-030-59830-3_19

A Deep Object Detection Method for Pineapple Fruit and Flower Recognition in Cluttered Background. / Wang, Chen; Zhou, Jun; Xu, Cheng yuan et al.
Pattern Recognition and Artificial Intelligence - International Conference, ICPRAI 2020, Proceedings. ed. / Yue Lu; Nicole Vincent; Pong Chi Yuen; Wei-Shi Zheng; Farida Cheriet; Ching Y. Suen. Springer Science and Business Media Deutschland GmbH, 2020. p. 218-227 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12068 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings › peer-review

TY - GEN

T1 - A Deep Object Detection Method for Pineapple Fruit and Flower Recognition in Cluttered Background

AU - Wang, Chen

AU - Zhou, Jun

AU - Xu, Cheng yuan

AU - Bai, Xiao

PY - 2020

Y1 - 2020

N2 - Natural initiation of pineapple flowers is not synchronized, which yields difficulties in yield prediction and the decision of harvest. Computer vision based pineapple detection system is an automated solution to address this issue. However, it is faced with significant challenges, e.g. pineapple flowers and fruits vary in size at different growing stages, the images are influenced by camera viewpoint, illumination conditions, occlusion and so on. This paper presents an approach for pineapple fruit and flower recognition using a state-of-the-art deep object detection model. We collected images from pineapple orchard using three different cameras and selected suitable ones to create a dataset. The experimental results show promising detection performance, with an mAP of 0.64 and F1 score of 0.69.

AB - Natural initiation of pineapple flowers is not synchronized, which yields difficulties in yield prediction and the decision of harvest. Computer vision based pineapple detection system is an automated solution to address this issue. However, it is faced with significant challenges, e.g. pineapple flowers and fruits vary in size at different growing stages, the images are influenced by camera viewpoint, illumination conditions, occlusion and so on. This paper presents an approach for pineapple fruit and flower recognition using a state-of-the-art deep object detection model. We collected images from pineapple orchard using three different cameras and selected suitable ones to create a dataset. The experimental results show promising detection performance, with an mAP of 0.64 and F1 score of 0.69.

KW - Deep learning

KW - Pineapple detection

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Pattern Recognition and Artificial Intelligence - International Conference, ICPRAI 2020, Proceedings

A2 - Lu, Yue

A2 - Vincent, Nicole

A2 - Yuen, Pong Chi

A2 - Zheng, Wei-Shi

A2 - Cheriet, Farida

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PB - Springer Science and Business Media Deutschland GmbH

T2 - 2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020

Y2 - 19 October 2020 through 23 October 2020

ER -

Wang C, Zhou J, Xu CY, Bai X. A Deep Object Detection Method for Pineapple Fruit and Flower Recognition in Cluttered Background. In Lu Y, Vincent N, Yuen PC, Zheng WS, Cheriet F, Suen CY, editors, Pattern Recognition and Artificial Intelligence - International Conference, ICPRAI 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 218-227. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-59830-3_19

A Deep Object Detection Method for Pineapple Fruit and Flower Recognition in Cluttered Background

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