Open AccessAn accurate pattern classification for empty fruit bunch based on the age profile of oil palm tree using neural network
Author(s) -
Wafi Aziz,
Afif Kasno,
Nurkamilia Kamarudin,
Zaidi Tumari,
Shahrieel Aras,
Herdy Rusnandi,
Kamal R. Musa
Publication year - 2019
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v9i6.pp5636-5643
Subject(s) - palm oil , artificial neural network , tree (set theory) , computer science , span (engineering) , class (philosophy) , artificial intelligence , pattern recognition (psychology) , process (computing) , mathematics , structural engineering , engineering , environmental science , agroforestry , mathematical analysis , operating system
This paper proposes an efficient method for pattern classification system of empty fruit bunch (EFB) by using a neural network technique. The main advantage of this method is the accuracy and speed of algorithm such that it can be computed rapidly with the proposed system. To test the effectiveness of the proposed method, 120 of EFB’s data with different ages and length that been obtained from Malaysian Palm Oil Board (MPOB) are use in the pattern classification process. In addition, there are three classes of EFB in this system, which are Class 1 (less than 7 year old), Class 2 (8 to 17 year old) and Class 3 (more than 17 year old). It is envisaged that the proposed method is very useful in classifying the EFB and 90% of the sample parameters are successfully classified to its class.