000			01965nam a2200253 a 4500
001			vtls000076552
003			KUKTEM
005			20251114204557.0
008			140107t2012 my a f m 000 0 eng d
020			_aTHE0002951(Local)
039		9	_a201905140836 _bzul _y201401070907 _znabilah
040			_aUMP
090			_aTA1637 .R33 2012 rs Bc.
100	0		_aRadhiah Zainon
245	1	0	_aPaddy disease detection system using image processing / _cRadhiah Zainon
260			_aKuantan, Pahang : _bUMP, _c2012
300			_axii, 106 p. : _bill. ; _c30 cm. + _e1 CD-ROM
502			_aProject paper (Bachelor of Computer Science (Software Engineering)) -- Universiti Malaysia Pahang – 2012
504			_aBibliography : p. 56-58
520	3		_aThe 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.
650		0	_aImage processing
650		0	_aRice _xDiseases and pests
999			_aVIRTUA40 _c4423 _d4429
999			_aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5040*5200*6500*6501*9992