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Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat.

机译:评估小麦中用于精确农业目的的无人机图像的准确性。

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High spatial resolution images taken by unmanned aerial vehicles (UAVs) have been shown to have the potential for monitoring agronomic and environmental variables. However, it is necessary to capture a large number of overlapped images that must be mosaicked together to produce a single and accurate ortho-image (also called an ortho-mosaicked image) representing the entire area of work. Thus, ground control points (GCPs) must be acquired to ensure the accuracy of the mosaicking process. UAV ortho-mosaics are becoming an important tool for early site-specific weed management (ESSWM), as the discrimination of small plants (crop and weeds) at early growth stages is subject to serious limitations using other types of remote platforms with coarse spatial resolutions, such as satellite or conventional aerial platforms. Small changes in flight altitude are crucial for low-altitude image acquisition because these variations can cause important differences in the spatial resolution of the ortho-images. Furthermore, a decrease of flying altitude reduces the area covered by each single overlapped image, which implies an increase of both the sequence of images and the complexity of the image mosaicking procedure to obtain an ortho-image covering the whole study area. This study was carried out in two wheat fields naturally infested by broad-leaved and grass weeds at a very early phenological stage. The geometric accuracy differences and crop line alignment among ortho-mosaics created from UAV image series were investigated while taking into account three different flight altitudes (30, 60 and 100 m) and a number of GCPs (from 11 to 45). The results did not show relevant differences in geo-referencing accuracy on the interval of altitudes studied. Similarly, the increase of the number of GCPs did not imply a relevant increase of geo-referencing accuracy. Therefore, the most important parameter to consider when choosing the flying altitude is the ortho-image spatial resolution required rather than the geo-referencing accuracy. Regarding the crop mis-alignment, the results showed that the overall errors were less than twice the spatial resolution, which did not break the crop line continuity at the studied spatial resolutions (pixels from 7.4 to 24.7 mm for 30, 60 and 100 m flying altitudes respectively) on the studied crop (early wheat). The results lead to the conclusion that a UAV flying at a range of 30 to 100 m altitude and using a moderate number of GCPs is able to generate ultra-high spatial resolution ortho-imagesortho-images with the geo-referencing accuracy required to map small weeds in wheat at a very early phenological stage. This is an ambitious agronomic objective that is being studied in a wide research program whose global aim is to create broad-leaved and grass weed maps in wheat crops for an effective ESSWM.
机译:无人飞行器(UAV)拍摄的高空间分辨率图像已被证明具有监测农艺和环境变量的潜力。但是,必须捕获大量的重叠图像,这些图像必须镶嵌在一起才能生成代表整个工作区域的单个且准确的正像(也称为正镶嵌图像)。因此,必须获取地面控制点(GCP)以确保镶嵌过程的准确性。无人飞行器正射镶嵌技术正成为早期特定地点杂草管理(ESSWM)的重要工具,因为使用其他类型的具有粗略空间分辨率的远程平台,对处于早期生长阶段的小植物(作物和杂草)的区分受到严重限制,例如卫星或传统的空中平台。飞行高度的微小变化对于低海拔图像的采集至关重要,因为这些变化可能会导致正交图像的空间分辨率发生重大差异。此外,飞行高度的减小会减小每个重叠图像的覆盖面积,这意味着图像序列的增加和图像镶嵌过程的复杂性增加,以获得覆盖整个研究区域的正交图像。这项研究是在很早的物候阶段,在两个自然地被阔叶和禾本科杂草侵染的麦田中进行的。研究了从无人机图像系列创建的正拼马赛克之间的几何精度差异和作物线对齐,同时考虑了三个不同的飞行高度(30、60和100 m)和多个GCP(从11到45)。结果并未显示在所研究的高度间隔上地理参考精度的相关差异。同样,GCP数量的增加并不意味着地理参考精度的相应提高。因此,选择飞行高度时要考虑的最重要参数是所需的正像空间分辨率,而不是地理参考精度。关于农作物错位,结果表明总体误差小于空间分辨率的两倍,在研究的空间分辨率下(30、60和100 m飞行时像素从7.4到24.7 mm不会破坏作物线的连续性)研究作物(早小麦)的海拔高度)。结果得出这样的结论:在30至100 m高度范围内飞行并使用适量的GCP的无人飞行器能够生成超高空间分辨率的正射影像图像,其地理参考精度可以绘制较小的在很早的物候期,小麦中的杂草。这是一个雄心勃勃的农业目标,正在广泛的研究计划中进行研究,其全球目标是在小麦作物中创建阔叶和草杂草图,以实现有效的ESSWM。

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