Agriculture stands as a pivotal factor in a nation’s economic advancement and the progress of human civilization. Yet, the prevalence of agricultural crop ailments exerts a detrimental influence and poses a substantial obstruction to agricultural output. Early detection of disease has become inevitable in this current scenario. Traditional practices entail long-term field monitoring and a time-consuming acquisition process. Compressive sensing, a novel sensing paradigm, acts as a great path that benefits both speed and quality of diagnosis. It allows signals to be acquired using fewer samples rate and still be able to reconstruct them without losing information. It can be leveraged to reduce the vast volume of data in data acquisition equipment. The work studies the viability of CS in the acquisition of plant images. The reconstruction performance is also evaluated in the proposed work.


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    Titel :

    Compressively Sensed Plant Images for Diagnosis of Diseases


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Sharma, Sanjay (Herausgeber:in) / Subudhi, Bidyadhar (Herausgeber:in) / Sahu, Umesh Kumar (Herausgeber:in) / Mathew, Nimmy Ann (Autor:in) / Krishna, Megha P. (Autor:in) / Jose, Renu (Autor:in)

    Kongress:

    International Conference on Robotics, Control, Automation and Artificial Intelligence ; 2022 November 24, 2022 - November 26, 2022



    Erscheinungsdatum :

    18.11.2023


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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