Weed is an unwanted plant that is found in the field. They can do some harm to the main crop, which can reduce their nutrition. There are many ways to kill weeds such as man power, herbicide spraying, etc. Each of these techniques have failed to find an appropriate way to eliminate the weeds. Such approaches have one or more disadvantages such as time consuming manpower, spraying herbicides that can harm the real crops. The level of herbicide usage has increased day by day to reduce the weeds. The use of herbicide \ will decline the crop yield. To address these disadvantages, a new system has been proposed to perform the real time identification of weeds in farm crops by using a deep learning method. The suggested solution works on real time farm crop images. This will accelerate the operation by eliminating the need to spray herbicides all over the field. The image is being captured by webcam and is processed by raspberry pi. To process the image, the OpenCV programming library and deep learning technique are used.


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    Titel :

    CNN based Synchronal recognition of Weeds in Farm Crops


    Beteiligte:
    Jogi, Yashaswini (Autor:in) / Rao, Preethi N (Autor:in) / Raksha (Autor:in) / Shetty, Sharadhi (Autor:in) / Shreekari (Autor:in)


    Erscheinungsdatum :

    05.11.2020


    Format / Umfang :

    387864 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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