Open AccessDefects Detection Algorithm of Harumanis Mango for Quality Assessment Using Colour Features Extraction
Author(s) -
Muhammad Nasri Abu Bakar,
Azizi Abdullah,
Norasmadi Abdul Rahim,
Haniza Yazid,
Norma Zakaria,
Omar S. Dahham,
W M F Wan Nik,
Nurhanani Abu Bakar,
Suzana Sulaiman,
Muhammad Ahmad,
Khursheed Ahmad,
N M Maliki,
S R Romle
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2107/1/012008
Subject(s) - support vector machine , computer science , artificial intelligence , segmentation , feature extraction , pattern recognition (psychology) , visual inspection , grading (engineering) , quality (philosophy) , engineering , philosophy , civil engineering , epistemology
Visual defects detection is one of the main problems in the post-harvest processing caused a major production and economic losses in agricultural industry. Manual fruits detection become easy when it is done in small amount, but the result is not consistent which will generate issue in fruit grading. A new fruit quality assessment system is necessary in order to increase the accuracy of classification, more consistencies, efficient and cost effective that would enable the industry to grow accordingly. In this paper, a method based on colour feature extraction for the quality assessment of Harumanis mango is proposed and experimentally validated. This method, including image background removal, defects segmentation and recognition and finally quality classification using Support Vector Machine (SVM) was developed. The results show that the experimental hardware system is practical and feasible, and that the proposed algorithm of defects detection is effective.