Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/454393
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dc.coverage.spatialInvestigation on methods of detection and classification of diseases in plant leaves
dc.date.accessioned2023-01-30T06:16:54Z-
dc.date.available2023-01-30T06:16:54Z-
dc.identifier.urihttp://hdl.handle.net/10603/454393-
dc.description.abstractDeveloping countries mainly rely on agricultural products for their economy. But plant yield is greatly affected due to diseases, which produce a huge financial loss as well as a threat to food security. The traditional approach involves naked eye observation of plant diseases by experts as well as by farmers. Rapid identification of disease is a challenging task as many farmers lack expertise and the disease vary from plant to plant as well as varies at each stage of growth, which requires continuous monitoring and is expensive for large farms. Further many farmers are unaware of non-native diseases. Moreover, rural areas lack experts which force them to go long distances to get a suitable solution. Recent technological advancements paved a way for a computer-aided approach to automatically detect and classify diseases. newlineVarious image processing methods are suggested in this study to identify and classify leaf diseases. The key goal of this study is to define and classify the unhealthy leaf portion, as well as to determine the best features that contribute to disease classification. A novel training function for neural network classifiers is also proposed to improve the classification accuracy of disease detection. In addition, a novel deep learning model is proposed to increase classification accuracy. The research study used a traditional PlantVillage dataset and a soursop dataset to accomplish these goals. newlineGray level co-occurrence matrix (GLCM) and statistical parameters were used in the study. A novel crest factor-based segmentation algorithm to detect leaf diseases in apple, cherry, strawberry, peach and corn, even in the presence of specular noise is proposed. newline
dc.format.extentxxi,215p.
dc.languageEnglish
dc.relationp.203-214
dc.rightsuniversity
dc.titleInvestigation on methods of detection and classification of diseases in plant leaves
dc.title.alternative
dc.creator.researcherFinney Daniel Shadrach S
dc.subject.keywordLeaf Disease
dc.subject.keywordGLCM Features
dc.subject.keywordDeep Learning
dc.description.note
dc.contributor.guideGunavathi K
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Information and Communication Engineering
dc.date.registered
dc.date.completed2021
dc.date.awarded2021
dc.format.dimensions21cm
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File58.71 kBAdobe PDFView/Open
02_prelim pages.pdf2.02 MBAdobe PDFView/Open
03_content.pdf251.42 kBAdobe PDFView/Open
04_abstract.pdf129.81 kBAdobe PDFView/Open
05_chapter 1.pdf601.9 kBAdobe PDFView/Open
06_chapter 2.pdf2.09 MBAdobe PDFView/Open
07_chapter 3.pdf1.72 MBAdobe PDFView/Open
08_chapter 4.pdf2.59 MBAdobe PDFView/Open
09_chapter 5.pdf1.78 MBAdobe PDFView/Open
10_chapter 6.pdf2.87 MBAdobe PDFView/Open
11_annexures.pdf127.94 kBAdobe PDFView/Open
80_recommendation.pdf72.09 kBAdobe PDFView/Open


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