Towards Palm Bunch Ripeness Classification Using Colour and Canny Edge Detection

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationComputational Science and Technology
EditorsRayner Alfred, Hiroyuki Iida, Haviluddin Haviluddin, Patricia Anthony
PublisherSpringer
Pages41-50
Number of pages10
ISBN (Electronic)9789813340695
ISBN (Print)9789813340688
DOIs
Publication statusPublished - 2021
Event7th International Conference on Computational Science and Technology 2020 - Pattaya, Thailand
Duration: 29 Aug 202030 Aug 2020

Publication series

NameLecture Notes in Electrical Engineering
Volume724
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference7th International Conference on Computational Science and Technology 2020
Abbreviated titleICCST 2020
Country/TerritoryThailand
CityPattaya
Period29/08/2030/08/20

Keywords

  • Canny edge
  • Colour detection
  • Empty sockets
  • Palm kernel
  • Ripeness

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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