Succulent plant image categorization suffers from subjectivity and limited number of available images, and these difficulties are intensified by the apparent lack of field surveys. In this study, we present a new approach to classify succulents using consumer-grade Unmanned Aerial Vehicles (UAVs) and deep learning. We applied various super-resolution algorithms with different image enhancement design strategies. In addition, we develop a new object detection model based on MambaIR and YOLOv10, achieving a mean average precision (mAP) of 0.851, outperforming other state-of-the-art object detection algorithms. The application of UAV s and super-resolution technology greatly facilitates the detection of succulents and provides a practical solution for precision crop monitoring.


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    Titel :

    UAV-Assisted Succulent Farmland Monitoring with Improved YOLOv10 and MambaIR


    Beteiligte:
    Li, Hui (Autor:in) / Xue, Feng (Autor:in) / Wang, Jiaqi (Autor:in) / Xi, Dianhan (Autor:in) / Liu, Yongying (Autor:in) / Zhang, Mowen (Autor:in) / Zhang, Guocheng (Autor:in) / Liu, Danhua (Autor:in) / Zhang, Chenyu (Autor:in) / Tao, Jianghan (Autor:in)


    Erscheinungsdatum :

    21.03.2025


    Format / Umfang :

    2634823 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch