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<p>Chrysanthemum growth and development in the Rayakottai, Krishnagiri (district of Tamil Nadu depend heavily on manual LED light operation in terms of photoperiodism and night-time pest elimination. Such a method is restricted in accuracy in timing (+/-18&#xa0;min), high power consumption (148 kWh per 60 days), and non-availability of automatic adaptation to pests. In this paper, a Vision-Assisted Mechatronic Illumination Control System (V-MICS) consisting of a GPS-enabled unmanned aerial vehicle, 50&#xa0;W multispectral lights, 120&#xa0;W light-emitting diodes based on sensors placed in the ground, and battery automation has been proposed and implemented in a field experiment conducted for 60 days in Krishnagiri and Rayakottai, India. From experimental findings, there has been an enhancement of flowering uniformity by 21.3% (from 63.0% to 76.4%), an increase in marketable stem length by 17.3% (34.2&#xa0;cm to 40.1&#xa0;cm), as well as a 32.8% decrease in damage caused by pests. There was also a decrease in the frequency of pesticide applications by 28.6%, as well as an energy savings of 38.0%. Mission completion rate of the UAV subsystem was 94.7% while hover accuracy was ± 0.15&#xa0;m using RTK GPS. The pest detection algorithm, based on YOLOv8, was able to adaptively adjust the lighting in less than 2&#xa0;s (mAP50: 91.3%). With thermal regulation experiments, there was an average of 3.3&#xa0;°C increase in canopy temperatures when below 10&#xa0;°C was experienced, thereby reducing mortality by about 40% due to cold stress. Economic analysis shows an estimated payback period of 18–24 months.</p>

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Drone-integrated illumination and power management for smart floriculture

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月5日

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  • Moulika Grandhi,
  • Sivayazi Kappagantula,
  • Giriraj Mannayee

摘要

Chrysanthemum growth and development in the Rayakottai, Krishnagiri (district of Tamil Nadu depend heavily on manual LED light operation in terms of photoperiodism and night-time pest elimination. Such a method is restricted in accuracy in timing (+/-18 min), high power consumption (148 kWh per 60 days), and non-availability of automatic adaptation to pests. In this paper, a Vision-Assisted Mechatronic Illumination Control System (V-MICS) consisting of a GPS-enabled unmanned aerial vehicle, 50 W multispectral lights, 120 W light-emitting diodes based on sensors placed in the ground, and battery automation has been proposed and implemented in a field experiment conducted for 60 days in Krishnagiri and Rayakottai, India. From experimental findings, there has been an enhancement of flowering uniformity by 21.3% (from 63.0% to 76.4%), an increase in marketable stem length by 17.3% (34.2 cm to 40.1 cm), as well as a 32.8% decrease in damage caused by pests. There was also a decrease in the frequency of pesticide applications by 28.6%, as well as an energy savings of 38.0%. Mission completion rate of the UAV subsystem was 94.7% while hover accuracy was ± 0.15 m using RTK GPS. The pest detection algorithm, based on YOLOv8, was able to adaptively adjust the lighting in less than 2 s (mAP50: 91.3%). With thermal regulation experiments, there was an average of 3.3 °C increase in canopy temperatures when below 10 °C was experienced, thereby reducing mortality by about 40% due to cold stress. Economic analysis shows an estimated payback period of 18–24 months.