Normal view MARC view ISBD view

Paddy disease detection system using image processing / Radhiah Zainon

By:

Radhiah Zainon

Material type: Text

TextPublication details: Kuantan, Pahang : UMP, 2012Description: xii, 106 p. : ill. ; 30 cm. + 1 CD-ROMISBN:

THE0002951(Local)

Subject(s):

Dissertation note: Project paper (Bachelor of Computer Science (Software Engineering)) -- Universiti Malaysia Pahang – 2012 Abstract: The main objectives of this research is to develop a prototype system for detect the paddy disease which are Paddy Blast Disease, Brown Spot Disease, Narrow Brown Spot Disease. This paper concentrate on the image processing techniques used to enhance the quality of the image and neural network technique to classify the paddy disease. The methodology involves image acquisition, pre-processing and segmentation, analysis and classification of the paddy disease. All the paddy sample will be passing through the RGB calculation before it proceed to the binary conversion. If the sample is in the range of normal paddy RGB, then it is automatically classify as type 4 which is Normal. Then, all the segmented paddy disease sample will be convert into the binary data in excel file before proceed through the neural network for training and testing. Consequently, by employing the neural network technique, the paddy diseases are recognized about 92.5 percent accuracy rates. This prototype has a very great potential to be further improved in the future.

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings ( 2 )
Title notes ( 3 )

Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Final Year Report	UMPLIB GAMBANG	TA1637 .R33 2012 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000079017
Final Year Report	UMPLIB GAMBANG	CD 7686 \| TA1637 .R33 2012 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000079018

Project paper (Bachelor of Computer Science (Software Engineering)) -- Universiti Malaysia Pahang – 2012

Bibliography : p. 56-58

The main objectives of this research is to develop a prototype system for detect the paddy disease which are Paddy Blast Disease, Brown Spot Disease, Narrow Brown Spot Disease. This paper concentrate on the image processing techniques used to enhance the quality of the image and neural network technique to classify the paddy disease. The methodology involves image acquisition, pre-processing and segmentation, analysis and classification of the paddy disease. All the paddy sample will be passing through the RGB calculation before it proceed to the binary conversion. If the sample is in the range of normal paddy RGB, then it is automatically classify as type 4 which is Normal. Then, all the segmented paddy disease sample will be convert into the binary data in excel file before proceed through the neural network for training and testing. Consequently, by employing the neural network technique, the paddy diseases are recognized about 92.5 percent accuracy rates. This prototype has a very great potential to be further improved in the future.