UAVs provide unprecedented data collection and consumption prospects, but integrating this data might be challenging. The data can be collected in any format, including pictures, video, and text. Mainly, the captured image would be utilized for different use cases. In addition, machine vision plays a significant role in unmanned drones by enabling the identification, categorization, and tracking of objects while the UAV is in flight. Without machine vision, a UAV could not interpret and interact with its environment, comprising building structures, vegetation, and varied terrain. Researchers are currently employing drones to monitor the agricultural circumstances above, allowing them to make more informed decisions on cultivation, fertilization, and harvesting. In precision agriculture, the most prevalent applications of machine vision include visual inspection of plant defect detection and defect positioning. The proposed model uses the decision tree algorithm to classify crop coverage, crop count, and segregate based on GPS Coordinates. The proposed model would also be used for quantifying the number of plants in a specific location using machine vision. Future work would outline the canopies for larger plants, such as trees or vegetables at a given location.


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    Titel :

    Machine Vision in UAV Data Analytics for Precision Agriculture


    Weitere Titelangaben:

    Transactions on Computer Systems & Networks


    Beteiligte:
    Karthikeyan, P. (Herausgeber:in) / Kumar, Sathish (Herausgeber:in) / Anbarasu, V. (Herausgeber:in) / Velusamy, Parthasarathy (Autor:in) / Rajendran, Santhosh (Autor:in) / John William, Alfred Daniel (Autor:in)


    Erscheinungsdatum :

    27.09.2023


    Format / Umfang :

    18 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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