This paper presents the design and implementation of a 10-inch quadcopter drone for smart agriculture applications, emphasizing component selection, power system configuration, and flight performance. The prototype achieves a thrust-to-weight ratio above 2:1 using Geprc 3115 900 kV motors, a 6S Li-Po battery, and a Geprc Mark4-10 airframe. Experimental results demonstrate stable hover with a maximum endurance of 12 min under a 3 kg payload, validating the design assumptions. During hover tests, the propulsion system drew a peak current of 26.4 A, consistent with the estimated power budget. Flight control was implemented using manually tuned PID gains, providing reliable performance in manual mode. These findings highlight the feasibility of deploying a cost-effective UAV platform for short-duration agricultural missions such as NDVI imaging and spot-spray tasks, while laying the groundwork for future AI-assisted control and autonomous operations.

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Optimizing Flight Time UAV for Collecting Big Data for Smart Agricultural Applications

  • Phat Nguyen Huu,
  • Nguyen Canh Duong,
  • Dung Hoang Tuan,
  • Khoa Nguyen Dac,
  • Nam Do Huy,
  • Quang Hoang Van

摘要

This paper presents the design and implementation of a 10-inch quadcopter drone for smart agriculture applications, emphasizing component selection, power system configuration, and flight performance. The prototype achieves a thrust-to-weight ratio above 2:1 using Geprc 3115 900 kV motors, a 6S Li-Po battery, and a Geprc Mark4-10 airframe. Experimental results demonstrate stable hover with a maximum endurance of 12 min under a 3 kg payload, validating the design assumptions. During hover tests, the propulsion system drew a peak current of 26.4 A, consistent with the estimated power budget. Flight control was implemented using manually tuned PID gains, providing reliable performance in manual mode. These findings highlight the feasibility of deploying a cost-effective UAV platform for short-duration agricultural missions such as NDVI imaging and spot-spray tasks, while laying the groundwork for future AI-assisted control and autonomous operations.