Towards palm bunch ripeness classification using colour and canny edge detection

Ian K.T. Tan; Yue Hng Lim; Nyen Ho Hon

doi:10.1007/978-981-33-4069-5_4

Towards palm bunch ripeness classification using colour and canny edge detection

Ian K.T. Tan, Yue Hng Lim, Nyen Ho Hon

Malaysia Sch of Information Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

2 Citations (Scopus)

Abstract

The ripeness of the farm-able palm fruits is an important factor in the production of quality palm oil. The work presented is an image processing implementation in the palm oil industry to eliminate human errors in the judgment of the ripeness of palm fruit bunches as well as to introduce automation. Various techniques were employed to obtain data from the images provided for the data mining process. The features used are the colour of the palm fruit bunches and the amount of edges representing visible leaves in the palm fruit bunches, indicating empty sockets. The project is able to achieve an accuracy of up to 79.11%.

Original language	English
Title of host publication	Computational Science and Technology - 7th ICCST 2020, Pattaya, Thailand, 29–30 August, 2020
Editors	Rayner Alfred, Hiroyuki Iida, Haviluddin Haviluddin, Patricia Anthony
Place of Publication	Singapore Singapore
Publisher	Springer
Pages	41-50
Number of pages	10
ISBN (Electronic)	9789813340695
ISBN (Print)	9789813340688
DOIs	https://doi.org/10.1007/978-981-33-4069-5_4
Publication status	Published - 2021
Event	International Conference on Computational Science and Technology 2020 - Pattaya, Thailand Duration: 29 Aug 2020 → 30 Aug 2020 Conference number: 7th https://link.springer.com/book/10.1007/978-981-33-4069-5 (Proceedings)

Publication series

Name	Lecture Notes in Electrical Engineering
Publisher	Springer
Volume	724
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	International Conference on Computational Science and Technology 2020
Abbreviated title	ICCST 2020
Country/Territory	Thailand
City	Pattaya
Period	29/08/20 → 30/08/20
Internet address	https://link.springer.com/book/10.1007/978-981-33-4069-5 (Proceedings)

Keywords

Canny edge
Colour detection
Empty sockets
Palm kernel
Ripeness

Access to Document

10.1007/978-981-33-4069-5_4

Cite this

Tan, I. K. T., Lim, Y. H., & Hon, N. H. (2021). Towards palm bunch ripeness classification using colour and canny edge detection. In R. Alfred, H. Iida, H. Haviluddin, & P. Anthony (Eds.), Computational Science and Technology - 7th ICCST 2020, Pattaya, Thailand, 29–30 August, 2020 (pp. 41-50). (Lecture Notes in Electrical Engineering; Vol. 724). Springer. https://doi.org/10.1007/978-981-33-4069-5_4

Tan, Ian K.T. ; Lim, Yue Hng ; Hon, Nyen Ho. / Towards palm bunch ripeness classification using colour and canny edge detection. Computational Science and Technology - 7th ICCST 2020, Pattaya, Thailand, 29–30 August, 2020. editor / Rayner Alfred ; Hiroyuki Iida ; Haviluddin Haviluddin ; Patricia Anthony. Singapore Singapore : Springer, 2021. pp. 41-50 (Lecture Notes in Electrical Engineering).

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title = "Towards palm bunch ripeness classification using colour and canny edge detection",

abstract = "The ripeness of the farm-able palm fruits is an important factor in the production of quality palm oil. The work presented is an image processing implementation in the palm oil industry to eliminate human errors in the judgment of the ripeness of palm fruit bunches as well as to introduce automation. Various techniques were employed to obtain data from the images provided for the data mining process. The features used are the colour of the palm fruit bunches and the amount of edges representing visible leaves in the palm fruit bunches, indicating empty sockets. The project is able to achieve an accuracy of up to 79.11%.",

keywords = "Canny edge, Colour detection, Empty sockets, Palm kernel, Ripeness",

author = "Tan, {Ian K.T.} and Lim, {Yue Hng} and Hon, {Nyen Ho}",

note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; International Conference on Computational Science and Technology 2020, ICCST 2020 ; Conference date: 29-08-2020 Through 30-08-2020",

year = "2021",

doi = "10.1007/978-981-33-4069-5_4",

language = "English",

isbn = "9789813340688",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer",

pages = "41--50",

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booktitle = "Computational Science and Technology - 7th ICCST 2020, Pattaya, Thailand, 29–30 August, 2020",

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Tan, IKT, Lim, YH & Hon, NH 2021, Towards palm bunch ripeness classification using colour and canny edge detection. in R Alfred, H Iida, H Haviluddin & P Anthony (eds), Computational Science and Technology - 7th ICCST 2020, Pattaya, Thailand, 29–30 August, 2020. Lecture Notes in Electrical Engineering, vol. 724, Springer, Singapore Singapore, pp. 41-50, International Conference on Computational Science and Technology 2020, Pattaya, Thailand, 29/08/20. https://doi.org/10.1007/978-981-33-4069-5_4

Towards palm bunch ripeness classification using colour and canny edge detection. / Tan, Ian K.T.; Lim, Yue Hng; Hon, Nyen Ho.
Computational Science and Technology - 7th ICCST 2020, Pattaya, Thailand, 29–30 August, 2020. ed. / Rayner Alfred; Hiroyuki Iida; Haviluddin Haviluddin; Patricia Anthony. Singapore Singapore: Springer, 2021. p. 41-50 (Lecture Notes in Electrical Engineering; Vol. 724).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

TY - GEN

T1 - Towards palm bunch ripeness classification using colour and canny edge detection

AU - Tan, Ian K.T.

AU - Lim, Yue Hng

AU - Hon, Nyen Ho

N1 - Conference code: 7th

PY - 2021

Y1 - 2021

N2 - The ripeness of the farm-able palm fruits is an important factor in the production of quality palm oil. The work presented is an image processing implementation in the palm oil industry to eliminate human errors in the judgment of the ripeness of palm fruit bunches as well as to introduce automation. Various techniques were employed to obtain data from the images provided for the data mining process. The features used are the colour of the palm fruit bunches and the amount of edges representing visible leaves in the palm fruit bunches, indicating empty sockets. The project is able to achieve an accuracy of up to 79.11%.

AB - The ripeness of the farm-able palm fruits is an important factor in the production of quality palm oil. The work presented is an image processing implementation in the palm oil industry to eliminate human errors in the judgment of the ripeness of palm fruit bunches as well as to introduce automation. Various techniques were employed to obtain data from the images provided for the data mining process. The features used are the colour of the palm fruit bunches and the amount of edges representing visible leaves in the palm fruit bunches, indicating empty sockets. The project is able to achieve an accuracy of up to 79.11%.

KW - Canny edge

KW - Colour detection

KW - Empty sockets

KW - Palm kernel

KW - Ripeness

UR - http://www.scopus.com/inward/record.url?scp=85103490890&partnerID=8YFLogxK

U2 - 10.1007/978-981-33-4069-5_4

DO - 10.1007/978-981-33-4069-5_4

M3 - Conference Paper

AN - SCOPUS:85103490890

SN - 9789813340688

T3 - Lecture Notes in Electrical Engineering

SP - 41

EP - 50

BT - Computational Science and Technology - 7th ICCST 2020, Pattaya, Thailand, 29–30 August, 2020

A2 - Alfred, Rayner

A2 - Iida, Hiroyuki

A2 - Haviluddin, Haviluddin

A2 - Anthony, Patricia

PB - Springer

CY - Singapore Singapore

T2 - International Conference on Computational Science and Technology 2020

Y2 - 29 August 2020 through 30 August 2020

ER -

Tan IKT, Lim YH, Hon NH. Towards palm bunch ripeness classification using colour and canny edge detection. In Alfred R, Iida H, Haviluddin H, Anthony P, editors, Computational Science and Technology - 7th ICCST 2020, Pattaya, Thailand, 29–30 August, 2020. Singapore Singapore: Springer. 2021. p. 41-50. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-33-4069-5_4

Towards palm bunch ripeness classification using colour and canny edge detection

Abstract

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Keywords

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