밭작물의 드론 방제 시 농약 비산량 예측 모델 개발
A Spray Drift Curve Model for Pesticide Drift Prediction during Drone Spraying Applications for Field Crop
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초록
Drift curves are widely used for predicting pesticide drift from ground applications because of their simplicity and practicality. However, developing drift curves suitable for the rapidly increasing drone-based aerial spraying remains challenging due to its complexity. This study assessed the applicability of existing drift curves for predicting spray drift in aerial applications and developed a new drift curve based on experimental data. Seventeen variables, including meteorological conditions, crop growth conditions, and drone operating factors, were analyzed for their influence on drift. The key factors were wind speed, atmospheric stability, crop height, and spraying height, differentiating from conventional models that emphasize temperature, wind speed, nozzle pressure, and flow rate. These differences highlight the effects of strong downwash and high-altitude spraying in drone applications. Additionally, the spatial pattern of drift showed a logarithmic decrease with distance, contrasting the exponential trend in ground-based models. The developed drift curve achieved high accuracy for predicting both airborne drift and ground deposition with R2 of 0.71 and 074, respectively, offering a cost—effective alternative to field experiments for efficient drift management.
Abstract
Drift curves are widely used for predicting pesticide drift from ground applications because of their simplicity and practicality. However, developing drift curves suitable for the rapidly increasing drone-based aerial spraying remains challenging due to its complexity. This study assessed the applicability of existing drift curves for predicting spray drift in aerial applications and developed a new drift curve based on experimental data. Seventeen variables, including meteorological conditions, crop growth conditions, and drone operating factors, were analyzed for their influence on drift. The key factors were wind speed, atmospheric stability, crop height, and spraying height, differentiating from conventional models that emphasize temperature, wind speed, nozzle pressure, and flow rate. These differences highlight the effects of strong downwash and high-altitude spraying in drone applications. Additionally, the spatial pattern of drift showed a logarithmic decrease with distance, contrasting the exponential trend in ground-based models. The developed drift curve achieved high accuracy for predicting both airborne drift and ground deposition with R2 of 0.71 and 074, respectively, offering a cost—effective alternative to field experiments for efficient drift management.
참고문헌 (28)
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