Fruit Classification Through Deep Learning: A Convolutional Neural Network Approach

Tahir Arshad; Min Jia; Qing Guo; Xuemai Gu; Xiaofeng Liu

doi:10.1007/978-981-13-9409-6_326

Fruit Classification Through Deep Learning: A Convolutional Neural Network Approach

Tahir Arshad
, Min Jia^*
, Qing Guo
, Xuemai Gu
, Xiaofeng Liu

^*Corresponding author for this work

School of Electronics and Information Engineering, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Convolutional Neural Network (CNN) is popular deep learning framework with vast applications in image classification, segmentation, object detection etc., and has attracted attention of the machine learning community at large. In this publication, we aim to propose a model for classification of fruits. Our model is novel as it applies the concept of local connectivity of patterns in neural networks and learns low level features while preserving information about the geometry of objects and shapes. We demonstrated the effectiveness of our approach on a fruits dataset with 63 classes. The obtained results effectively demonstrate the local representation capacity of CNNs. We achieved test set accuracy of 96.63% and training set accuracy of 96.42%, which effectively exemplify the effectiveness of CNNs for this class of problems.

Original language	English
Title of host publication	Communications, Signal Processing, and Systems - Proceedings of the 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019
Editors	Qilian Liang, Wei Wang, Xin Liu, Zhenyu Na, Min Jia, Baoju Zhang
Publisher	Springer
Pages	2671-2677
Number of pages	7
ISBN (Print)	9789811394089
DOIs	https://doi.org/10.1007/978-981-13-9409-6_326
State	Published - 2020
Externally published	Yes
Event	8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019 - Urumqi, China Duration: 20 Jul 2019 → 22 Jul 2019

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	571 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019
Country/Territory	China
City	Urumqi
Period	20/07/19 → 22/07/19

Keywords

Classification
Convolutional neural networks
Deep learning
Image statistics
Machine learning

Access to Document

10.1007/978-981-13-9409-6_326

Cite this

Arshad, T., Jia, M., Guo, Q., Gu, X., & Liu, X. (2020). Fruit Classification Through Deep Learning: A Convolutional Neural Network Approach. In Q. Liang, W. Wang, X. Liu, Z. Na, M. Jia, & B. Zhang (Eds.), Communications, Signal Processing, and Systems - Proceedings of the 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019 (pp. 2671-2677). (Lecture Notes in Electrical Engineering; Vol. 571 LNEE). Springer. https://doi.org/10.1007/978-981-13-9409-6_326

Arshad, Tahir ; Jia, Min ; Guo, Qing et al. / Fruit Classification Through Deep Learning : A Convolutional Neural Network Approach. Communications, Signal Processing, and Systems - Proceedings of the 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019. editor / Qilian Liang ; Wei Wang ; Xin Liu ; Zhenyu Na ; Min Jia ; Baoju Zhang. Springer, 2020. pp. 2671-2677 (Lecture Notes in Electrical Engineering).

@inproceedings{9e62339883864f7aabaf0ab5148dbe4f,

title = "Fruit Classification Through Deep Learning: A Convolutional Neural Network Approach",

abstract = "Convolutional Neural Network (CNN) is popular deep learning framework with vast applications in image classification, segmentation, object detection etc., and has attracted attention of the machine learning community at large. In this publication, we aim to propose a model for classification of fruits. Our model is novel as it applies the concept of local connectivity of patterns in neural networks and learns low level features while preserving information about the geometry of objects and shapes. We demonstrated the effectiveness of our approach on a fruits dataset with 63 classes. The obtained results effectively demonstrate the local representation capacity of CNNs. We achieved test set accuracy of 96.63\% and training set accuracy of 96.42\%, which effectively exemplify the effectiveness of CNNs for this class of problems.",

keywords = "Classification, Convolutional neural networks, Deep learning, Image statistics, Machine learning",

author = "Tahir Arshad and Min Jia and Qing Guo and Xuemai Gu and Xiaofeng Liu",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Singapore Pte Ltd.; 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019 ; Conference date: 20-07-2019 Through 22-07-2019",

year = "2020",

doi = "10.1007/978-981-13-9409-6\_326",

language = "英语",

isbn = "9789811394089",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer",

pages = "2671--2677",

editor = "Qilian Liang and Wei Wang and Xin Liu and Zhenyu Na and Min Jia and Baoju Zhang",

booktitle = "Communications, Signal Processing, and Systems - Proceedings of the 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019",

address = "德国",

}

Arshad, T, Jia, M , Guo, Q, Gu, X & Liu, X 2020, Fruit Classification Through Deep Learning: A Convolutional Neural Network Approach. in Q Liang, W Wang, X Liu, Z Na, M Jia & B Zhang (eds), Communications, Signal Processing, and Systems - Proceedings of the 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019. Lecture Notes in Electrical Engineering, vol. 571 LNEE, Springer, pp. 2671-2677, 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019, Urumqi, China, 20/07/19. https://doi.org/10.1007/978-981-13-9409-6_326

Fruit Classification Through Deep Learning: A Convolutional Neural Network Approach. / Arshad, Tahir; Jia, Min ; Guo, Qing et al.
Communications, Signal Processing, and Systems - Proceedings of the 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019. ed. / Qilian Liang; Wei Wang; Xin Liu; Zhenyu Na; Min Jia; Baoju Zhang. Springer, 2020. p. 2671-2677 (Lecture Notes in Electrical Engineering; Vol. 571 LNEE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Fruit Classification Through Deep Learning

T2 - 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019

AU - Arshad, Tahir

AU - Jia, Min

AU - Guo, Qing

AU - Gu, Xuemai

AU - Liu, Xiaofeng

PY - 2020

Y1 - 2020

N2 - Convolutional Neural Network (CNN) is popular deep learning framework with vast applications in image classification, segmentation, object detection etc., and has attracted attention of the machine learning community at large. In this publication, we aim to propose a model for classification of fruits. Our model is novel as it applies the concept of local connectivity of patterns in neural networks and learns low level features while preserving information about the geometry of objects and shapes. We demonstrated the effectiveness of our approach on a fruits dataset with 63 classes. The obtained results effectively demonstrate the local representation capacity of CNNs. We achieved test set accuracy of 96.63% and training set accuracy of 96.42%, which effectively exemplify the effectiveness of CNNs for this class of problems.

AB - Convolutional Neural Network (CNN) is popular deep learning framework with vast applications in image classification, segmentation, object detection etc., and has attracted attention of the machine learning community at large. In this publication, we aim to propose a model for classification of fruits. Our model is novel as it applies the concept of local connectivity of patterns in neural networks and learns low level features while preserving information about the geometry of objects and shapes. We demonstrated the effectiveness of our approach on a fruits dataset with 63 classes. The obtained results effectively demonstrate the local representation capacity of CNNs. We achieved test set accuracy of 96.63% and training set accuracy of 96.42%, which effectively exemplify the effectiveness of CNNs for this class of problems.

KW - Classification

KW - Convolutional neural networks

KW - Deep learning

KW - Image statistics

KW - Machine learning

UR - https://www.scopus.com/pages/publications/85084729036

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DO - 10.1007/978-981-13-9409-6_326

M3 - 会议稿件

AN - SCOPUS:85084729036

SN - 9789811394089

T3 - Lecture Notes in Electrical Engineering

SP - 2671

EP - 2677

BT - Communications, Signal Processing, and Systems - Proceedings of the 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019

A2 - Liang, Qilian

A2 - Wang, Wei

A2 - Liu, Xin

A2 - Na, Zhenyu

A2 - Jia, Min

A2 - Zhang, Baoju

PB - Springer

Y2 - 20 July 2019 through 22 July 2019

ER -

Arshad T, Jia M , Guo Q, Gu X, Liu X. Fruit Classification Through Deep Learning: A Convolutional Neural Network Approach. In Liang Q, Wang W, Liu X, Na Z, Jia M, Zhang B, editors, Communications, Signal Processing, and Systems - Proceedings of the 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019. Springer. 2020. p. 2671-2677. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-13-9409-6_326

Fruit Classification Through Deep Learning: A Convolutional Neural Network Approach

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Keywords

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