Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/331828
Full metadata record
DC FieldValueLanguage
dc.coverage.spatial
dc.date.accessioned2021-07-15T05:43:58Z-
dc.date.available2021-07-15T05:43:58Z-
dc.identifier.urihttp://hdl.handle.net/10603/331828-
dc.description.abstractnewlineThe survival of human beings is generally based on the proper productivity of agriculture.The paddy plant is considered as a major planting crop in improving the economical level of our country. Nowadays, the yield level of paddy crop might be minimized due to several diseases. Bacteria, fungi, virus and certain harmful insects are the main causative agents for such disease occurrence on the paddy crop. The diseases which affect the early stage of the paddy crops influences in the whole stage of crop cultivation. In early days of agriculture, the manual detection of diseases have been carried out by farmers. Naked eye inspection is commonly performed in manually detecting the diseases in the crops. In this primitive method, enormous time is needed for classifying the diseases and normally leads to certain errors. Image processing is one of the emerging techniques for identifying and classifying the different types of diseases and it overcomes the issues encountered during the manual detection of diseases. Image processing technique solves several issues involved in the cultivation of crops including, recognition and classification of plant diseases, discrimination of certain weeds and disease forecasting. newlineThe steps involved in the image processing are image acquisition, pre-processing, and segmentation of image, feature extraction and classification. Four different types of rice plant diseases namely, bacterial blight, sheath rot, brown spot and blast diseases are identified and classified in this thesis with the help of image processing and machine learning techniques. K-means clustering is used for segmentation of the diseased and healthy portion of the leaves. The features of colour and texture are extracted in the stage of feature extraction. Four different classifiers namely, ANN, DNN, KNN and optimized DNN classifier with Jaya optimization are used in this thesis for identifying and classifying the occurrence of diseases in paddy leaves in both agricultural and green house environment.
dc.format.extenti-xii, 126
dc.languageEnglish
dc.relation
dc.rightsuniversity
dc.titleDisease Detection in Paddy Crops of Rural India Using Machine Learning Techniques
dc.title.alternative
dc.creator.researcherRamesh, S
dc.subject.keywordEngineering
dc.subject.keywordEngineering and Technology
dc.subject.keywordEngineering Electrical and Electronic
dc.description.note
dc.contributor.guideVydeki, D
dc.publisher.placeVellore
dc.publisher.universityVIT University
dc.publisher.institutionSchool of Electronics Engineering-VIT-Chennai
dc.date.registered2015
dc.date.completed2020
dc.date.awarded
dc.format.dimensions
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:School of Electronics Engineering-VIT-Chennai

Files in This Item:
File Description SizeFormat 
01_title page.pdfAttached File104.63 kBAdobe PDFView/Open
02_signedcopyof declaraton & certificate.pdf64.7 kBAdobe PDFView/Open
03_abstract.pdf41.21 kBAdobe PDFView/Open
04_contents.pdf46.92 kBAdobe PDFView/Open
05_list of tables.pdf41.5 kBAdobe PDFView/Open
06_list of figures.pdf44.15 kBAdobe PDFView/Open
07_acknowledgement.pdf42.16 kBAdobe PDFView/Open
08_chapter_01.pdf4.09 MBAdobe PDFView/Open
09_chapter_02.pdf449.4 kBAdobe PDFView/Open
10_chapter_03.pdf7.99 MBAdobe PDFView/Open
11_chapter_04.pdf198.11 kBAdobe PDFView/Open
12_chapter_05.pdf142.67 kBAdobe PDFView/Open
13_chapter_06.pdf45.5 kBAdobe PDFView/Open
14_chapter_07.pdf50.58 kBAdobe PDFView/Open
15_references.pdf100.65 kBAdobe PDFView/Open
16_list of publications.pdf42.74 kBAdobe PDFView/Open
80_recommendation.pdf155.8 kBAdobe PDFView/Open


Items in Shodhganga are licensed under Creative Commons Licence Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).

Altmetric Badge: