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dc.contributor.authorChaya, S-
dc.contributor.authorTanki, Saim (13ET60)-
dc.contributor.authorMalik, Ajmal (14ET28)-
dc.contributor.authorKhan, Junaid (14ET24)-
dc.contributor.authorKhan, Aziz (13ET22)-
dc.date.accessioned2019-05-30T05:20:30Z-
dc.date.available2019-05-30T05:20:30Z-
dc.date.issued2019-05-
dc.identifier.urihttp://www.aiktcdspace.org:8080/jspui/handle/123456789/3046-
dc.descriptionSubmitted in partial fulfillment of the requirements for the degree of Bachelor of Engineering 2019en_US
dc.description.abstractIndia, thecountrywherethemainsourceofincomeisfromagriculture. Farmersgrowavarietyofcropsbasedontheirrequirement.Sincetheplants su er fromthedisease,theproductionofcropdecreasesduetoinfections caused byseveraltypesofdiseasesonitsleaf,fruit,andstem.Leafdiseases are mainlycausedbybacteria,fungi,virusetc.Diseasesareoftendi cult to control.Diagnosisofthediseaseshouldbedoneaccuratelyandproper actions shouldbetakenattheappropriatetime.ImageProcessingisthe trending techniqueindetectionandclassi cationofplantleafdisease.This workdescribeshowtoautomaticallydetectleafdiseases.Thegivensystem will provideafast,spontaneous,preciseandveryeconomicalmethodin detecting andclassifyingleafdiseases.Thispaperisenvisionedtoassistin the detectingandclassifyingleafdiseasesusingMulticlassSVMclassi cation technique.First,thea ectedregionisdiscoveredusingsegmentationbyK- means clustering,thenfeatures(colorandtexture)areextracted.Lastly, classi cation techniqueisappliedindetectingthetypeofleafdisease. Keywords: Image Processing,Leafdiseasesdetection,K-meansclustering, featureextraction,MulticlassSVMClassi cation.en_US
dc.language.isoenen_US
dc.publisherAIKTCen_US
dc.relation.ispartofseriesPE0494;-
dc.subjectProject Report - EXTCen_US
dc.titleWeed and himayaritic of leaf detection system with I.Pen_US
dc.typeProject Reporten_US
Appears in Collections:EXTC Engineering - Project Reports

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