Autonomous Drone Design for Precision Agriculture

  • Authors

    • Dr. Elena Petrova Associate Professor, Moscow State University, Russia Author

    DOI:

    https://doi.org/10.67228/30716357/IJMRSE-2020PI8E6R

    Published 06-02-2020

  • Precision Agriculture, Autonomous Drones, UAV Systems, Sensor Fusion, Multispectral Imaging, Path Planning, Crop Monitoring, Machine Learning, Remote Sensing, Smart Farming

    Issue

    Vol. 3 No. 1 (2020)

    Section

    Articles

    How to Cite

    Autonomous Drone Design for Precision Agriculture. (2020). International Journal of Modern Research in Science & Engineering, 3(1), 01-18. https://doi.org/10.67228/30716357/IJMRSE-2020PI8E6R

  • Abstract

    Precision agriculture focuses on improving crop yield, optimizing resource use, and reducing environmental impact through data-driven decision-making. Recent advancements in UAVs, artificial intelligence, embedded systems, and remote sensing have enabled the use of autonomous drones in farming.  This paper presents the design of an autonomous drone system for precision agriculture, integrating intelligent sensors, adaptive navigation, and real-time analytics. The system addresses key agricultural challenges such as climate change, soil degradation, water scarcity, and rising operational costs by replacing labor-intensive and time-consuming manual inspections with efficient aerial monitoring. The proposed modular drone system includes flight control, multispectral imaging, computer vision, IoT connectivity, and machine learning models. It emphasizes durability, energy efficiency, fault tolerance, and autonomous decision-making. Key features include sensor fusion, path planning, obstacle avoidance, and adaptive mission scheduling. Experimental results demonstrate improved monitoring accuracy, higher coverage, and reliable data collection compared to traditional methods. The study highlights the potential of autonomous drones in promoting sustainable agriculture through efficient resource management, early stress detection, and large-scale farm monitoring.

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