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Object-based early monitoring of a grass weed in a grass crop using high resolution UAV imagery

机译:使用高分辨率无人机图像对草作物中的草杂草进行基于对象的早期监测

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Sorghum halepense (johnsongrass) is a perennial weed with a vegetative reproductive system and one of the most competitive weeds in maize showing a spatial distribution in compact patches. When maize is irrigated, successive weed emergences occur in the early phenological phases of the crop, which require several herbicide applications. Our aim was to provide an accurate tool for an early detection and mapping of johnsongrass patches and delineate the actual surface area requiring a site-specific herbicide treatment based on the weed coverage. This early detection represents a major challenge in actual field scenarios because both species are in the Poaceae family, and show analogous spectral patterns, an extraordinarily similar appearance and a parallel phenological evolution. To solve this, an automatic OBIA (object-based-image-analysis) procedure was developed to be applied on orthomosaicked images using visible (red-green-blue bands) and multispectral (red-green-blue and near infrared bands) cameras collected by an unmanned aerial vehicle (UAV) that flew at altitudes of 30, 60 and 100 m on two maize fields. One of our first phases was the generation of accurate orthomosaicked images of an herbaceous crop such as maize, which presented a repetitive pattern and nearly no invariant parameters to conduct the aerotriangulation. Here, we show that high-quality orthomosaicks were produced from both cameras and that they were able to be the first step for mapping the johnsongrass patches. The most accurate weed maps were obtained using the multispectral camera at an altitude of 30 m in both fields. These maps were then used to design a site-specific weed management program, and we demonstrated that potential herbicide savings ranged from 85 to 96 %. Our results showed that accurate and timely maps of johnsongrass patches in maize can be a key element in achieving site-specific and sustainable herbicide applications for reducing spraying herbicides and costs.
机译:高粱halepense(johnsongrass)是一种多年生杂草,具有营养繁殖系统,是玉米中最具竞争力的杂草之一,在紧凑的斑块中表现出空间分布。灌溉玉米后,在作物的物候早期阶段便会出现连续的杂草出苗,这需要使用几种除草剂。我们的目标是为约翰逊草斑块的早期检测和制图提供准确的工具,并根据杂草覆盖率描绘需要进行特定位点除草剂处理的实际表面积。这种早期发现在实际田间环境中是一个重大挑战,因为这两个物种都属于禾本科,并且显示出相似的光谱模式,异常相似的外观和平行的物候演变。为了解决这个问题,开发了一种自动OBIA(基于对象的图像分析)程序,该程序将使用收集的可见(红-绿-蓝波段)和多光谱(红-绿-蓝和近红外波段)相机应用于矫正畸形的图像由无人驾驶飞机(UAV)在两个玉米田上分别以30、60和100 m的高度飞行。我们的第一个阶段之一是生成像玉米这样的草本作物的准确正畸图像,该图像呈现出重复的模式并且几乎没有不变的参数来进行空气三角测量。在这里,我们证明了这两种相机都生产了高质量的正翅目袋装,并且它们能够成为绘制johnsongrass斑块的第一步。在两个场中使用多光谱相机在30 m的高度上获得了最准确的杂草图。然后将这些地图用于设计特定地点的杂草管理程序,我们证明了潜在的除草剂节省范围为85%至96%。我们的结果表明,准确,及时地绘制玉米johnsongrass斑图可成为实现特定地点和可持续的除草剂应用以减少喷洒除草剂和降低成本的关键要素。

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