드론 방제 비산예측 모델 구축
Development of a prediction model for spray drift using agricultural drones
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Can Aerial Spraying Be Done Safely?
Can Aerial Spraying Be Done Safely?
With the rise of drone-based pesticide spraying, concerns about pesticide drift—chemicals spreading beyond the target area—are growing. To address this, our research team developed a test system that can repeatedly and accurately measure drift.
Following international standards (ISO 22866 and ASABE S561.1), we built a test bench at Chonnam National University’s Naju field site and verified its performance through field experiments.
This system allows for more consistent and controlled testing, regardless of outdoor conditions. It also enables the creation of a drift database under various scenarios, helping to improve the reliability and precision of pesticide drift evaluations.
How Can Drones Spray More Evenly and Safely?
As drone (UAV) technology advances, aerial spraying is becoming more common in agriculture. Drones offer numerous benefits over traditional equipment, but they also present challenges, especially in terms of spray drift and uneven coverage.
To solve these issues, it's essential to understand how the air pushed down by drone propellers (known as downwash) affects the spray's landing on the ground.
In this study, two different agricultural drones were tested under various flying conditions. The goal was to measure how air flows beneath the drones and how spray droplets are distributed.
The results showed:
When the drone hovers, spray is mostly concentrated right below it.
When the drone is flying, the spray spreads more evenly, especially within a distance equal to the radius of the propeller blades (R).
An effective spray width of three times the blade radius (3R) gave much more uniform coverage, with less than 16% variation.
These findings can help improve drone spraying techniques, making them more precise, efficient, and environmentally safe.
Identification of major drift factors
Identification of major drift factors
To understand and reduce this drift, our team conducted 43 field tests, measuring pesticide levels in the air, on the ground, and on crops. We examined 18 different factors such as weather, crop type, drone model, spray technique, and liquid properties.
Key findings include:
Wind speed was the biggest factor near the spray area.
Crop type affected how far the spray travelled downwind.
Atmospheric stability also played a significant role.
Ground deposition varied with downwind crop type, temperature, and spray volume.
The spray concentration influenced all types of drift and deposition.
These insights will help improve drone spraying methods, making them safer and more accurate.
A New Way to Predict Pesticide Drift from Drones
A New Way to Predict Pesticide Drift from Drones
Traditional drift curves are commonly used to estimate how far pesticides travel during spraying from ground equipment. They're simple and practical—but not well suited for drones, which spray from higher altitudes and create strong downward airflow.
This study evaluated whether existing drift curves work for drones and developed a new model based on field experiments. We analysed 17 factors, including weather, crop conditions, and drone operation details. The most important factors were wind speed, air stability, crop height, and spray height—different from ground-based models that focus more on temperature and nozzle settings.
We also found that pesticide drift from drones decreases in a logarithmic pattern with distance, unlike the exponential decrease seen in ground spraying.
The new drift curve accurately predicted both airborne drift (R² = 0.71) and ground deposition (R² = 0.74), providing a reliable and cost-effective tool for managing pesticide drift in drone applications.
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