Conventional farming practices in Indonesia face numerous challenges, including low productivity, weather unpredictability, and overuse of natural resources. Autonomous drones offer a promising solution, enhancing efficiency and precision in agricultural operations. The implementation of a monitoring system facilitates real-time assessment of drone performance, including adherence to flight routes, speed, altitude, and detection of abnormalities. Utilizing the Random Forest (RF) method, this system conducts eight classifications of drone data with remarkable accuracy, reaching up to 98%. This innovative approach holds significant potential for revolutionizing the agricultural industry in Indonesia, driving towards sustainable and technologically advanced farming practices.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Autonomous Drone Technology based Random Forest Classifier for Revolutionizing Agriculture




    Publication date :

    2024-02-21


    Size :

    413427 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    AI Integration in Drone Technology: Revolutionizing Applications in Agriculture, Security, and Beyond

    Rehman, Faisal / Rabail, Asna / Sajjad, Muhammad Hamza et al. | Springer Verlag | 2025


    Wireless Autonomous Drone Charging Hub: Revolutionizing Unmanned Aircraft Connectivity

    Dhanasekar, R. / Vijayaraja, L. / Premkumar, R. et al. | IEEE | 2023


    Revolutionizing Mobility:The Latest Advancements in Autonomous Vehicle Technology

    Narisetty, Venkata Sai Chandra Prasanth / Maddineni, Tejaswi | ArXiv | 2024

    Free access

    Importance of Drone Technology in Agriculture

    Natarajan, Karuppiah / Karthikeyan, R. / Rajalingam, S. | Wiley | 2023


    Autonomous robotic drone system for mapping forest interiors

    V. Karjalainen / N. Koivumäki / T. Hakala et al. | DOAJ | 2024

    Free access