• Login
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    Statistics

    View Usage StatisticsView Google Analytics Statistics

    Sensors-based prediction of plant diseases using neural network

    Thumbnail
    View/Open
    201811085.pdf (4.571Mb)
    Date
    2020
    Author
    Kumar, Manish
    Metadata
    Show full item record
    Abstract
    Plant diseases could cause a loss to agricultural production and the economy; hence there is a need to develop prediction models for fast plant disease detection and assessment methods. These methods must not affect plant growth when deployed. Not only plant diseases can be detected, but also the production can be improved drastically. Fungal infection is the most dominant but can be controlled by appropriate measures if detected at an early stage. The paper aims to develop an expert system for the prediction of various fungal diseases like powdery mildew, anthracnose, rust, and root rot/leaf blight. Hence, we propose an artificial neural network for the classification of the diseases. This paper validates a real-time dataset, captured at DA-IICT, Gandhinagar, India. The results give high classification accuracy for the proposed model. The implementation of this work proves the feasibility of using this technique for faster plant disease detection at an affordable cost. As soon as early detection is achievable in the field, the damage to the crop due to these diseases could be reduced.
    URI
    http://drsr.daiict.ac.in//handle/123456789/990
    Collections
    • M Tech Dissertations [923]

    Resource Centre copyright © 2006-2017 
    Contact Us | Send Feedback
    Theme by 
    Atmire NV
     

     


    Resource Centre copyright © 2006-2017 
    Contact Us | Send Feedback
    Theme by 
    Atmire NV