Classification of piper nigrum samples using machine learning techniques: A comparison

D.N.F, Awang Iskandar and Nuraya, Abdullah and Alvin, W. Yeo and Shapiee, Abdul Rahman and Ahmad, Hadinata Fauzi and Rubiyah, Baini (2013) Classification of piper nigrum samples using machine learning techniques: A comparison. In: 3rd International Conference on Digital Information Processing and Communications, ICDIPC 2013, 30 January 2013 through 1 February 2013, Islamic Azad UniversityDubai; United Arab Emirates.

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Pepper is a key export of the state of Sarawak (Malaysian Borneo). At present, processed pepper berries are graded manually. This process is time consuming and error prone as it is very much dependent on the experience of the pepper grader. To overcome these weaknesses, we propose an automated Pepper Grading System which employs image processing and machine learning using image features and moisture content data of the pepper berries. In this paper, we present our findings of using twenty machine learning algorithms to classify the pepper berries into its respective grades based on image features, which is part of our research work towards an automated Pepper Grading System. We found that Rotation Forest was the best classifier

Item Type: Proceeding (Paper)
Uncontrolled Keywords: Agricultural sciences, Classifiers, Image processing and analysis, Machine learning,unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, research, Universiti Malaysia Sarawak
Subjects: T Technology > TJ Mechanical engineering and machinery
Divisions: Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Depositing User: Saman
Date Deposited: 06 Jun 2017 02:32
Last Modified: 06 Jun 2017 02:32

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