果树叶片雾滴沉积量检测系统设计与试验
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国家重点研发计划项目(2016YFD0200700-2016YFD0200706)


Detecting System Design of Droplet Deposition on Fruit Leaves
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    摘要:

    为了获取植保无人机喷药后雾滴在果树叶片表面的沉积量,设计了面向植保无人机果树低空施药的果树叶片雾滴沉积量检测系统。该系统由LWS型叶面湿度传感器、数据传输模块、上位机检测软件组成。通过LWS型叶面湿度传感器的标定试验,建立了电导率为553μS/cm自来水、860μS/cm甲基硫菌灵溶液、1525μS/cm磷酸二氢钾叶面肥溶液的回归方程,通过分光光度计验证试验验证了方程的准确性。之后,建立基于ZigBee的传感器系统数据无线传输网络。同时,利用Qt编写了具有数据分析和显示功能的上位机程序,建立了完整的果树叶片雾滴沉积量检测系统。最后,利用WSZ-4X型植保无人机在樱桃果园中进行了检测系统与水敏纸的对比试验。对比结果显示,使用两种方法获得的雾滴沉积密度曲线的拟合度可达0.9266。对于单个测量点的雾滴沉积密度,其平均误差为22.8%。在果园中进行试验时,受风速和无人机气流等环境因素的影响,传感器和水敏纸的雾滴分布会出现一定的差异,忽略环境因素影响,可认为两种方法在樱桃果园中测量得到的雾滴沉积密度一致性较好,而使用果树叶片雾滴沉积量检测系统可以更加快速、方便、实时地采集农药雾滴在叶面上的沉积量。

    Abstract:

    In order to obtain the deposition value of the droplets on the leaves of the fruit trees after spraying, a detecting system of droplet deposition on fruit leaves used in low altitude pesticide spraying was developed. The system consisted of dielectric leaf wetness sensor, data transmission module and host computer detection software. Through the calibration experiment of dielectric leaf wetness sensor, the regression equations of three solutions, which were tap water with 553μS/cm of EC, thiophonate-methyl with 860μS/cm of EC, and KH2PO4 foliar fertilizer with 1525μS/cm of EC, were established. The accuracies of the regression equations were verified by spectra photometry. Then, a wireless data transmission sensor system based on ZigBee was developed. Moreover, the Qt platform was used to write the host computer program including data analysis and display functions. Finally, the contrast test between water sensitive paper and this system was carried out by using WSZ-4X type plant protection UAV in a cherry orchard. The results showed that the coefficient of correlation between the density values of the droplet deposition obtained by two methods was 0.9626. For a single measurement point, the average error of the droplet deposition density was 22.8%. In the orchard experiments, under the influence of the wind speed and the unmanned aerial vehicle air flow and other environmental factors, the droplet distribution on the sensor and the water sensitive paper appeared certain differences. The consistency of the droplet deposition density obtained by two methods was preferable if the influence of environmental factors was ignored. However, the droplet deposition on leaves was acquired quickly, conveniently, timely and simply by using the developed system.

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杨玮,郝子源,李民赞,张旭.果树叶片雾滴沉积量检测系统设计与试验[J].农业机械学报,2017,48(s1):8-14. YANG Wei, HAO Ziyuan, LI Minzan, ZHANG Xu. Detecting System Design of Droplet Deposition on Fruit Leaves[J]. Transactions of the Chinese Society for Agricultural Machinery,2017,48(s1):8-14.

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  • 收稿日期:2017-07-10
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  • 在线发布日期: 2017-12-10
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