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A Real-Time Weed Mapping and Precision Herbicide Spraying System for Row Crops

机译:行作物实时杂草测绘和精密除草剂喷雾系统

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摘要

This study developed and field tested an automated weed mapping and variable-rate herbicide spraying (VRHS) system for row crops. Weed detection was performed through a machine vision sub-system that used a custom threshold segmentation method, an improved particle swarm optimum (IPSO) algorithm, capable of segmenting the field images. The VRHS system also used a lateral histogram-based algorithm for fast extraction of weed maps. This was the basis for determining real-time herbicide application rates. The central processor of the VRHS system had high logic operation capacity, compared to the conventional controller-based systems. Custom developed monitoring system allowed real-time visualization of the spraying system functionalities. Integrated system performance was then evaluated through field experiments. The IPSO successfully segmented weeds within corn crop at seedling growth stage and reduced segmentation error rates to 0.1% from 7.1% of traditional particle swarm optimization algorithm. IPSO processing speed was 0.026 s/frame. The weed detection to chemical actuation response time of integrated system was 1.562 s. Overall, VRHS system met the real-time data processing and actuation requirements for its use in practical weed management applications.
机译:这项研究开发并现场测试了用于大田作物的自动杂草测绘和可变速率除草剂喷洒(VRHS)系统。通过机器视觉子系统执行杂草检测,该子系统使用自定义阈值分割方法,这是一种能够对野外图像进行分割的改进的粒子群优化(IPSO)算法。 VRHS系统还使用基于横向直方图的算法快速提取杂草图。这是确定实时除草剂施用量的基础。与传统的基于控制器的系统相比,VRHS系统的中央处理器具有较高的逻辑运算能力。定制开发的监控系统允许实时查看喷涂系统功能。然后,通过现场实验评估了集成系统的性能。 IPSO在幼苗生长阶段成功地对玉米作物内的杂草进行了分割,并将分割错误率从传统粒子群优化算法的7.1%降低到了0.1%。 IPSO处理速度为0.026 s /帧。杂草对综合系统化学驱动响应时间的检测为1.562 s。总的来说,VRHS系统满足了在实际杂草管理应用中使用的实时数据处理和启动要求。

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