밭작물의 드론 방제 시 농약 비산량 예측 모델 개발

• Published in: Hanguk hwangyeong nonghak hoeji Vol. 43; no. 43; pp. 336 - 347
• Main Authors: 이세연, Se-yeon Lee, 박진선, Jinseon Park, 이채린, Chae-rin Lee, Kehinde Favour Daniel, 박지연, Ji-yeon Park, 유승화, Seung-hwa Yu, 이춘구, Chun-gu Lee, 홍세운, Se-woon Hong
• Format: Journal Article
• Language: Korean
• Published: 한국환경농학회 01.04.2024
• Subjects: Aerial spraying; Airborne drift; Atmospheric stability; Ground deposition; Logarithmic equation; 농학; Ground deposition; Aerial spraying; Airborne drift; Logarithmic equation; Atmospheric stability
• ISSN: 1225-3537; 2233-4173
• DOI: 10.5338/KJEA.2024.43.32

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 R 2 of 0.71 and 074, respectively, offering a cost―effective alternative to field experiments for efficient drift management.