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    Autonomous VTOL tail-sitter for precision crop health monitoring

    Autor/es: Martínez Lluís, Manuel
    Director/es: Padrón Nápoles, Víctor Manuel
    Palabra/s clave: Precision agriculture; 3D printing; Autonomous UAV; VTOL Tail-sitter; Multispectral imaging
    Titulación: Grado en Ingeniería Aeroespacial
    Fecha de defensa: 2024-07
    Tipo de contenido: TFG
    URI: https://hdl.handle.net/20.500.12880/9271
    Resumen:
    Precision agriculture is undergoing a transformative shift towards maximizing crop yield while minimizing resource consumption. Traditional methods of crop monitoring often lack accuracy and are labour-intensive. The integration of UAVs equipped with multispectral cameras has revolutionized this field, offering a non-invasive, efficient means of surveying agricultural landscapes to perceive detailed plant health information. The primary objective is to create a drone capable of autonomously executing flight missions from take-off to landing, with minimum interventions required from the operator. By harnessing the power of multispectral imagery, the drone facilitates precise monitoring of crop health parameters, enabling farmers to make informed decisions regarding irrigation, fertilization, and pest control strategies. Methodologically, the project follows a systematic approach encompassing design, 3D printing, integration of electronics, and development of an autonomous flight system. Results demonstrate the successful creation of an autonomous VTOL tail-sitter drone capable of efficiently capturing multispectral data. In conclusion, this project contributes to the advancement of precision agriculture by offering a robust and efficient tool for crop monitoring and management. The fully autonomous capabilities of the drone, coupled with its ability to provide actionable insights through multispectral imagery, hold great promise for enhancing agricultural productivity and sustainability.
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    Nombre: TFG_ManuelMartinezLluis.pdf
    Tamaño: 4.240Mb
    Formato: PDF
    Tipo de contenido: TFG

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