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Plant' Disease Detection Using Image Processing CNN Tecnology – Grapevine, Strawberry, Apple, Cherry, Corn, Peach

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dc.contributor.author Hassan, Md. Rakibul
dc.date.accessioned 2022-02-10T03:56:10Z
dc.date.available 2022-02-10T03:56:10Z
dc.date.issued 2021-09-09
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/7066
dc.description.abstract Depression My Project ‘PLANT DISEASE DETECTION USING CNN TECHNOLOGY’ works with disease detection and identification for Grapevine, Strawberry based on image processing. There is very big improvement was been made in the field of image preparing and Artificial Intelligence and its applications are used in many different parts of designing. People have already entered the time of digitalization. We caught pictures with the help of advanced cameras and the more clear picture are produce then better ,useable and productive the result’s and outcome. In this report I have done an arrangement of fresh, somewhere infected and fully sick leaves. I have been utilizing the HSI shading model to make group of my properties and furthermore I have utilized Neural Network (CNN) Tools for AI to investigate the outcomes. Although measurements for the plant features are fundamental elements for plant science and research also related applications. The information’s are related to plant features that principally useful for the applications used in plant agricultural research and growth modeling also on farm production. Past direct measurement methods are generally simple and not so much reliable, on the other hand they are very much time consuming, cumbersome laborious. The proposed vision on the basis of methods that are efficient in observing and detecting the exterior disease and other features. In this present state, image processing algorithms are developing quickly, to detect plants diseases by recognizing and identifying the particular color frame of the individual affected area. Eventually, the rotted area is subdivide from an image. Then the area of decayed leaf chunk was deduced from the Grapevine and Strawberry plants featured data. In my case, the outcomes have showed an encouraging execution of this automated vision-based system, I have achieved 91% accuracy in practice with complimentary validation. en_US
dc.language.iso en_US en_US
dc.publisher Daffodil International University en_US
dc.subject Plant disease en_US
dc.subject Image processing en_US
dc.subject CNN tecnology en_US
dc.title Plant' Disease Detection Using Image Processing CNN Tecnology – Grapevine, Strawberry, Apple, Cherry, Corn, Peach en_US
dc.type Article en_US

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