Article Per Year (5 Year)
p-Index From 2021 - 2026
0
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Shafaf Ibrahim
Universiti Teknologi MARA Cawangan Melaka (Kampus Jasin)
Computer Science & IT Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Rice grain classification using multi-class support vector machine (SVM) Shafaf Ibrahim; Nurul Amirah Zulkifli; Nurbaity Sabri; Anis Amilah Shari; Mohd Rahmat Mohd Noordin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (489.854 KB) | DOI: 10.11591/ijai.v8.i3.pp215-220

Abstract

Presently, the demands for rice are increasing. This will affects the need for producing and sorting rice grain in faster and exceed the normal requirement. However, the manual rice classification using naked eyes are not very accurate and only professionals are able to do it. Machine learning is found to be a suitable technique for rice classification in producing an accurate result and faster solution. Thus, a study on the classification of rice grain using an image processing technique is presented. The rice grain image went through the pre-processing process which includes the grayscale and binary conversion, and segmentation before the feature extraction process. Four attributes of shape descriptor which are area, perimeter, major axis length, and minor axis length and three attributes of color descriptor which are hue, saturation and value were extracted from each rice grain image. In another note, a Multi-class Support Vector Machine (SVM) is used to classify the three types of rice grain which are basmathi, ponni and brown rice. The performance of the proposed study is evaluated to 90 testing images which returned 92.22% of classification accuracy. The study is expected to assist the Agrotechnology industry in automatic classification of rice grain in the future.
Automatic classification of paddy leaf disease Shafaf Ibrahim; Nurnazihah Wahab; Ahmad Firdaus Ahmad Fadzil; Nur Nabilah Abu Mangshor; Zaaba Ahmad
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp767-774

Abstract

RiceisastaplefoodinmostoftheAsiancountries.Itisanimportantcrop, andoverhalfoftheworldpopulationreliesonitforfood.However,paddy leafdiseasecanaffectboththequalityandquantityofpaddyinagriculture production.Theclassificationofpaddyleafdiseaseisanimportantand urgenttaskasitdestroysabout10%to15%ofproductioninAsia.Thus,a studyonautomaticclassificationofpaddyleafdiseaseusingimage processingispresented.Featureextractiontechniquesofcolor,texture,and shapewereimplementedtoanalyzethecharacteristicsofthepaddyleaf disease.Inanother note,aSupportVector Machine(SVM)isused toclassify thefourtypesofpaddyleafdiseasewhicharethebrownspot,bacterialleaf blight,tungrovirus,andleaf scald.Theperformanceofthe proposedstudyis evaluatedto160testingimageswhichreturned86.25%ofclassification accuracy.Theoutcomeofthisstudyisexpectedtoassisttheagrotechnology industryinearlydetectionofpaddyleafdiseaseinwhichanappropriate actioncould be taken accordingly.
Co-Authors Ahmad Firdaus Ahmad Fadzil Anis Amilah Shari Mohd Rahmat Mohd Noordin Nur Nabilah Abu Mangshor Nurbaity Sabri Nurnazihah Wahab Nurul Amirah Zulkifli Zaaba Ahmad