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<p>This study presents innovative drone-based remote sensing techniques for agricultural monitoring, emphasizing advanced imaging systems, autonomous operations, and yield estimation. A hexacopter equipped with a customized pulley system was used to deploy multispectral and thermal cameras, enabling precise imaging of crops without propeller downwash interference. The captured data were analyzed to calculate vegetation indices, such as normalized difference vegetation index (NDVI), for assessing crop health, and to estimate production yield using digital surface model (DSM) analysis. Significant findings include the identification of healthy and stressed vegetation in rice and bean fields, with NDVI values ranging from 0.31 to 0.97, and the differentiation of non-vegetated areas with values below 0.3. DSM data were employed to measure canopy height, calculate canopy volume, and determine coverage percentages, providing insights into potential yield. High-yield areas, such as sites with canopy volumes exceeding 1200&#xa0;m<sup>3</sup> and coverage above 85%, were identified, while low-yield areas showed sparse vegetation or bare soil. Additionally, an infrared beacon-based precision landing system was developed to ensure autonomous and accurate drone landings in GPS-denied environments, expanding operational versatility. This integrated approach demonstrates a scalable framework for smart farming, offering efficient crop monitoring and production yield estimation to enhance sustainable agriculture.</p>

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Advanced Techniques in Agricultural Drone Applications: Multispectral Imaging, Thermal Analysis, Precision Landing, and Customized Pulley Systems for Enhanced Remote Sensing

  • 期刊论文

  • 发表日期：

    2025年7月16日

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  • Byeong Gyu Gang

摘要

This study presents innovative drone-based remote sensing techniques for agricultural monitoring, emphasizing advanced imaging systems, autonomous operations, and yield estimation. A hexacopter equipped with a customized pulley system was used to deploy multispectral and thermal cameras, enabling precise imaging of crops without propeller downwash interference. The captured data were analyzed to calculate vegetation indices, such as normalized difference vegetation index (NDVI), for assessing crop health, and to estimate production yield using digital surface model (DSM) analysis. Significant findings include the identification of healthy and stressed vegetation in rice and bean fields, with NDVI values ranging from 0.31 to 0.97, and the differentiation of non-vegetated areas with values below 0.3. DSM data were employed to measure canopy height, calculate canopy volume, and determine coverage percentages, providing insights into potential yield. High-yield areas, such as sites with canopy volumes exceeding 1200 m3 and coverage above 85%, were identified, while low-yield areas showed sparse vegetation or bare soil. Additionally, an infrared beacon-based precision landing system was developed to ensure autonomous and accurate drone landings in GPS-denied environments, expanding operational versatility. This integrated approach demonstrates a scalable framework for smart farming, offering efficient crop monitoring and production yield estimation to enhance sustainable agriculture.