Jackfruit trees are usually tall, making manual monitoring methods inefficient for jackfruit cultivation. In this paper, we propose utilizing an unmanned aerial vehicle (UAV) equipped with the YOLOv8 algorithm for jackfruit growth monitoring, alongside Visual-Inertial Navigation System Fusion (Vins-Fusion) for positioning and integrated front-end and back-end trajectory optimization techniques. We collect sample data by photographing jackfruit and searching for images on the internet. The data are annotated using LabelImg and then used to train the deep learning model. The trained model is deployed on the UAV, which is equipped with a Jetson module and a depth camera, to achieve aerial detection of jackfruit ripeness and determine if they are rotten, using the YOLOv8 algorithm. Finally, we discuss the experimental results, focusing on training loss, validation loss, precision, recall, and mean average precision (mAP), and flight recognition capability. The results demonstrate that the UAV can efficiently, autonomously, and accurately detect the ripeness of jackfruit and determine whether it is rotten.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Monitoring Jackfruit Growth Based on YOLOv8 with Unmanned Aerial Vehicles


    Contributors:
    Han, Haoyuan (author) / Wang, Zijun (author) / Wu, Di (author)


    Publication date :

    2024-10-18


    Size :

    12004589 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    ITD-YOLOv8: An Infrared Target Detection Model Based on YOLOv8 for Unmanned Aerial Vehicles

    Xiaofeng Zhao / Wenwen Zhang / Hui Zhang et al. | DOAJ | 2024

    Free access

    Recognition of Maize Tassels Based on Improved YOLOv8 and Unmanned Aerial Vehicles RGB Images

    Jiahao Wei / Ruirui Wang / Shi Wei et al. | DOAJ | 2024

    Free access

    UAV Detection for Aerial Vehicles using YOLOv8

    Sairam, D.V. / Bhuvaneswari, R / Chokkalingam, SP | IEEE | 2024



    Unmanned aerial vehicle laser charging tracking system using improved YOLOv8

    ZHONG YUANCHANG / CHEN YU / LI DALIN et al. | European Patent Office | 2024

    Free access