COTTON YIELD ESTIMATION USING VERY HIGH-RESOLUTION DIGITAL IMAGES ACQUIRED WITH A LOW-COST SMALL UNMANNED AERIAL VEHICLE
COTTON YIELD ESTIMATION USING VERY HIGH-RESOLUTION DIGITAL IMAGES ACQUIRED WITH A LOW-COST SMALL UNMANNED AERIAL VEHICLE
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DOI：
10.13031/trans.59.11831
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发表时间：
2016-01-01
影响因子：
1.5
通讯作者：
Ebelhar, M. W.
中科院分区：
文献类型：
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作者：
Huang, Y.;Brand, H. J.;Ebelhar, M. W.
估产是作物管理中的一项重要任务。传统的方法成本高、耗时长，难以推广到相对较大的领域。遥感技术可以在任何尺度上快速覆盖一块土地。卫星遥感被用于大规模的对地观测。利用载人飞机在相对较高的海拔(500米)进行遥感，很难达到大田规模精准农业所需的空间分辨率。地面系统通常用于点测量，并受现场条件的限制。无人机(UAV)为高分辨率遥感提供了一个独特的平台，基于无人机的遥感系统可以用来以具有成本效益的方式估计作物产量。本研究的目的是为精准棉花种植开发和评估棉花产量的新方法。2014年，试验田在密西西比州斯通维尔附近的一块棉田里铺设。以5种不同的施氮量对地块进行施氮肥，以产生棉花产量差异。利用从廉价的小型多旋翼无人机获取的高分辨率数字图像(2.7 cm像素(-1))，采用两种方法估计棉花产量：(1)使用来自棉田多幅数字图像的三维点云数据估计棉花株高，从而估计产量；(2)在收获前从落叶棉田的数字图像的背景中分割棉铃特征，然后利用估计的棉花小区单位覆盖率估计产量。结果表明，利用价格低廉的小型无人机进行低空遥感估产，可以准确地估算出棉花的株高(R2=0.43，而人工测量株高估产的R2=0.42)。结果进一步表明，该方法通过拉普拉斯图像处理估计每一块地块的棉铃覆盖率，并将少数光照条件较差的地块作为异常值(R2=0.83)，可以提供可靠的棉花产量估计。本研究可为农业研究和作物生产中的棉花估产提供参考，为其他作物估产提供参考。
Yield estimation is a critical task in crop management. Traditional methods are costly, time-consuming, and difficult to expand to a relatively large field. Remote sensing can provide quick coverage over a field at any scale. Satellite remote sensing is used for large-scale earth observation. Remote sensing with manned airplanes at relatively high altitudes (>500 m) has difficulty achieving the spatial resolution required for field-scale precision farming. Ground-based systems are typically used for point measurements and are restricted to field conditions. Unmanned aerial vehicles (UAVs) provide a unique platform for high-resolution remote sensing, and UAV-based remote sensing systems can be used to estimate crop yield in a cost-effective manner. The objective of this study was to develop and evaluate new methods for estimation of cotton yield for precision cotton farming. Experimental plots were laid out in a cotton field near Stoneville, Mississippi, in 2014. Nitrogen fertilizer was applied to the plots at five different rates to generate cotton yield variation. Two methods were employed to estimate cotton yield using very high-resolution digital images (2.7 cm pixel(-1)) acquired from an inexpensive small multirotor UAV: (1) using three-dimensional point cloud data derived from multiple digital images of the cotton field to estimate cotton plant height and hence estimate yield, and (2) segmenting cotton boll signatures from the background of the digital images of the defoliated cotton field just prior to harvest and then estimating yield with the estimated cotton plot unit coverage. The results indicated that low-altitude remote sensing with an inexpensive small UAV can be used to estimate cotton yield accurately through estimation of plant height (R-2 = 0.43, compared with R-2 = 0.42 for yield estimation through manually measured plant height). The results further indicated that the method can offer reliable cotton yield estimation through estimation of cotton boll coverage in each plot with Laplacian image processing while considering a few plots with poor light condition as outliers (R-2 = 0.83). This study could benefit yield estimation of cotton, with similar methods used for other crops, in agricultural research and crop production.