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Mobile application for classifying palm oil bunch using RGB and artificial neural network / Sayyidatina Al Hurul Aina Alzahari

By:

Sayyidatina Al Hurul Aina Alzahari

Material type: Text

TextPublication details: Kuantan, Pahang : UMP, 2016Description: xvi, 99 p. : ill. (some col.) ; 30 cm. + 1 CD-ROMISBN:

THE0001256(Local)

Subject(s):

Dissertation note: Project paper (Bachelor of Computer Science (Software Engineering) With Honours) -- Universiti Malaysia Pahang – 2016 Abstract: Malaysia is known as the second largest producer of palm oil in the world. The current procedure in palm oil mills and in palm plantation, grading the palm oil ripeness is done manually by human graders. This conventional method is subjective, inefficient and inconsistence because human graders has their own techniques, expertise and experience which may vary from each other. Hence, this method affects the quality of palm oil production and also takes longer time for grading. This method must be replaced with technology-based because nowadays technology can reduce human effort, to increase productivity, and more reliable compared to conventional method. In this project, a new method was introduced by developing Android mobile application which comprises with Artificial Neural Network (ANN) engine to classify the palm oil bunch based on RGB colour model. This project aim is to get the accuracy of the ripeness of palm oil bunch with mobile technology. The purpose of this mobile application is to distinguish between the four different classes of palm oil bunch which are unripe, half-ripe, ripe, and overripe. The ripeness palm oil bunch is based on different colour intensity. Hence, this mobile application will use the image of palm oil bunch that has been captured from user's mobile telephone and analyze the images correspondent to human eye and mind. The mobile application is developed for the image processing to make calculation of the mean colour based on RGB colour model and use ANN to decision making to test the data and make the classification for the palm oil bunch. The program that has been developed is expected to distinguish the four different classes of palm oil bunch automatically. This project provides a very reliable technique to standardize the palm oil bunch grading system.

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Holdings ( 2 )
Title notes ( 4 )

Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Final Year Report	UMPLIB PEKAN	FSKKP .S29 2016 r Bc. (Browse shelf(Opens below))	1	Not for loan		0000117592
Final Year Report	UMPLIB PEKAN	CD 10699 \| FSKKP .S29 2016 r Bc. (Browse shelf(Opens below))	1	Not for loan		0000117593

Faculty of Computer Systems and Software Engineering

Project paper (Bachelor of Computer Science (Software Engineering) With Honours) -- Universiti Malaysia Pahang – 2016

Bibliography : p. 88-91

Malaysia is known as the second largest producer of palm oil in the world. The current procedure in palm oil mills and in palm plantation, grading the palm oil ripeness is done manually by human graders. This conventional method is subjective, inefficient and inconsistence because human graders has their own techniques, expertise and experience which may vary from each other. Hence, this method affects the quality of palm oil production and also takes longer time for grading. This method must be replaced with technology-based because nowadays technology can reduce human effort, to increase productivity, and more reliable compared to conventional method. In this project, a new method was introduced by developing Android mobile application which comprises with Artificial Neural Network (ANN) engine to classify the palm oil bunch based on RGB colour model. This project aim is to get the accuracy of the ripeness of palm oil bunch with mobile technology. The purpose of this mobile application is to distinguish between the four different classes of palm oil bunch which are unripe, half-ripe, ripe, and overripe. The ripeness palm oil bunch is based on different colour intensity. Hence, this mobile application will use the image of palm oil bunch that has been captured from user's mobile telephone and analyze the images correspondent to human eye and mind. The mobile application is developed for the image processing to make calculation of the mean colour based on RGB colour model and use ANN to decision making to test the data and make the classification for the palm oil bunch. The program that has been developed is expected to distinguish the four different classes of palm oil bunch automatically. This project provides a very reliable technique to standardize the palm oil bunch grading system.