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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    Title :

    UAV-Assisted Succulent Farmland Monitoring with Improved YOLOv10 and MambaIR


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


    Publication date :

    2025-03-21


    Size :

    2634823 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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