The purpose of this project is to provide a helping hand to our farmers in their difficult work of farming. The idea is to use swarm technology (one main drone rest 3–4 worker drones) in the agricultural field to solve problems related to farming. Today AI sensors have made the work for humans very easy. Similarly, in this project, drones will be equipped with different sensors like hyperspectral, thermal and LiDAR. These sensors will be used to solve problems like weed detection, drought condition detection, water stress, nitrogen content etc. Hyperspectral sensors are more precise and accurate than multispectral sensors in detecting minerals and vegetation, which will help in maintaining the plant nutrient status, identifying plant disease, water quality assessment and surface chemical composition. Thermal sensors measure the surface temperature of land and objects and create their thermal images for further analysis [1]. Then theses created thermal images are analyzed for identifying any heat stress, water stress, and plant metabolism from their canopy temperature. LiDAR sensors are laser equipped and use a laser beam to create 3D models of crops to identify drought stress and to optimize water use. LiDAR sensors are used to measure the vegetation level, the topography of the ground underneath. As an initiative, authors tried to apply this project in a small scale but if this initiative become successful, this can be applied in a large scale to provide more and more help to our farmers.


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    Title :

    Smart Agriculture: The Age of Drones in Agriculture


    Additional title:

    Lecture Notes in Civil Engineering


    Contributors:

    Conference:

    International Conference on Unmanned Aerial System in Geomatics ; 2019 ; Roorkee, India April 06, 2019 - April 07, 2019


    Published in:

    Proceedings of UASG 2019 ; Chapter : 34 ; 415-424


    Publication date :

    2020-02-23


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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