Please use this identifier to cite or link to this item: https://elibrary.khec.edu.np:8080/handle/123456789/424
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dc.contributor.advisorEr. Shree Ram Khaitu-
dc.contributor.authorAakash Chaudhary (740301)-
dc.contributor.authorAarju Chaulagain (740302)-
dc.contributor.authorGaurav Chapagain (740315)-
dc.contributor.authorSusan Tyata (740346)-
dc.contributor.authorSubek Khadka (740344)-
dc.date.accessioned2022-09-21T10:23:52Z-
dc.date.available2022-09-21T10:23:52Z-
dc.date.issued2022-08-
dc.identifier.urihttps://elibrary.khec.edu.np/handle/123456789/424-
dc.description.abstractWith increase in population the need for food is on rise, in such circumstances, plant diseases prove to be a major threat to agricultural produce and result in disastrous consequences for farmers. Early detection of plant disease can help in ensuring food security and controlling financial losses. Wheat is a widely cultivated crop whose seed is a grain used all over the world as a staple food. The wheat diseases are harmful to wheat production, The wheat diseases are generally viral, bacterial, fungal, rust etc. There are many types of disease which are presents in wheat leaf. Recently, wheat disease detection through leaf image and data processing techniques are used extensively and in expensive system especially for assisting farmers in monitoring the big plantation area. Machine learning techniques are described for wheat leaf disease detection and its classification also with its recommendation of its medicine.en_US
dc.language.isoenen_US
dc.subjectPlant disease, image processing, image acquisition, segmentation, feature extraction, classification, Medicine recommendationen_US
dc.titleWheat leaf Disease Detectionen_US
dc.typeTechnical Reporten_US
local.college.nameKhwopa Engineering College-
local.degree.departmentDepartment of Computer-
local.degree.nameBE Computer-
local.degree.levelBE-
local.item.accessionnumberD.1235-
Appears in Collections:PU Computer Report

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