Hanif Adhitya Saiful, Han Xiongzhe, Yu Seung-Hwa, Han Cheolwoo, Baek Sun Wook, Lee Chun-Gu, Lee Dae-Hyun, Kang Yeong Ho
College of Agricultural and Life Sciences, Interdisciplinary Program in Smart Agriculture, Kangwon National University, Chuncheon, Republic of Korea.
College of Agricultural and Life Sciences, Department of Biosystem Engineering, Kangwon National University, Chuncheon, Republic of Korea.
Front Plant Sci. 2023 Aug 21;14:1235548. doi: 10.3389/fpls.2023.1235548. eCollection 2023.
In the past decade, unmanned aerial spraying systems (UASS) have emerged as an effective crop treatment platform option, competing with other ground vehicle treatments. The development of this platform has provided an effective spraying system that can be used on all crop types and in all weather conditions. However, related research has not been able to develop a UASS that can be operated in windy conditions with a low drift percentage.
In this research, spraying was simulated in an indoor flight simulator by considering flight speed, altitude, wind speed, wind direction, rotor rotation, interval, spraying pattern, and nozzle type, which were used as the parameters affecting the output value of the coefficient of variation (CV) of spraying. These parameters were referenced as properties that occur in the field, and using machine learning methods, the CV value was used as a dataset to develop a model that can execute pump opening by controlling the flow rate. There are four machine learning methods used, i.e. random forest regression, gradient boosting, ada boost, and automatic relevance determination regression which are compared with simple linear regression and ridge regression as linear regression.
The results revealed that the random forest regression model was the most accurate, with R2 of 0.96 and root mean square error (RMSE) of 0.04%. The developed model was used to simulate spraying with pump opening A, which connects two nozzles in front, and pump opening AB, which connects all four nozzles.
Using the logic based on CV value and pesticide quantity, the model can execute the pump opening against the environment and UASS operation.
在过去十年中，无人航空喷雾系统（UASS）已成为一种有效的作物处理平台选项，可与其他地面车辆处理方式相竞争。该平台的发展提供了一种有效的喷雾系统，可用于所有作物类型和所有天气条件。然而，相关研究尚未开发出一种能在有风条件下以低漂移率运行的UASS。
在本研究中，通过考虑飞行速度、高度、风速、风向、旋翼旋转、间隔、喷雾模式和喷嘴类型，在室内飞行模拟器中模拟喷雾，这些参数被用作影响喷雾变异系数（CV）输出值的参数。这些参数被作为田间出现的特性进行参考，并使用机器学习方法，将CV值用作数据集来开发一个可通过控制流速执行泵开启的模型。使用了四种机器学习方法，即随机森林回归、梯度提升、自适应增强和自动相关性确定回归，并将其与作为线性回归的简单线性回归和岭回归进行比较。
结果表明，随机森林回归模型最为准确，R2为0.96，均方根误差（RMSE）为0.04%。所开发的模型用于模拟使用泵开启A（连接前面两个喷嘴）和泵开启AB（连接所有四个喷嘴）的喷雾。
基于CV值和农药用量的逻辑，该模型可针对环境和UASS操作执行泵开启。
