Sorghum (Sorghum bicolor L. Moench) is a C4 tropical grass that plays an essential role in providing nutrition to humans and livestock, particularly in marginal rainfall environments. The timing of head development and the number of heads per unit area are key adaptation traits to consider in agronomy and breeding but are time consuming and labor intensive to measure. We propose a two-step machine-based image processing method to detect and count the number of heads from high-resolution images captured by unmanned aerial vehicles (UAVs) in a breeding trial. To demonstrate the performance of the proposed method, 52 images were manually labeled; the precision and recall of head detection were 0.87 and 0.98, respectively, and the coefficient of determination (R2) between the manual and new methods of counting was 0.84. To verify the utility of the method in breeding programs, a geolocation-based plot segmentation method was applied to pre-processed ortho-mosaic images to extract >1000 plots from original RGB images. Forty of these plots were randomly selected and labeled manually; the precision and recall of detection were 0.82 and 0.98, respectively, and the coefficient of determination between manual and algorithm counting was 0.56, with the major source of error being related to the morphology of plants resulting in heads being displayed both within and outside the plot in which the plants were sown, i.e., being allocated to a neighboring plot. Finally, the potential applications in yield estimation from UAV-based imagery from agronomy experiments and scouting of production fields are also discussed.
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机译:高粱(Sorghum bicolor L. Moench)是一种C4热带草,在为人类和牲畜提供营养,尤其是在边际降雨环境中,起着至关重要的作用。头部发育的时间和单位面积的头部数量是农艺学和育种中要考虑的关键适应性状,但耗时且劳动强度大。我们提出了一种基于机器的两步式图像处理方法,以在繁殖试验中从无人飞行器(UAV)捕获的高分辨率图像中检测并计数头部的数量。为了演示该方法的性能,手动标记了52张图像。头部检测的精确度和召回率分别为0.87和0.98,手动计数和新计数方法之间的测定系数(R 2 sup>)为0.84。为了验证该方法在育种程序中的实用性,将基于地理位置的图块分割方法应用于预处理的正镶嵌图像,以从原始RGB图像中提取> 1000个图块。这些地块中有40个是随机选择并手动标记的。检测的精度和召回率分别为0.82和0.98,手动计数与算法计数之间的确定系数为0.56,主要的误差源与植物的形态有关,导致在图的内部和外部显示头部在其中播种了植物,即被分配到相邻的地块。最后,还讨论了在农机实验和生产田地侦查中基于无人机图像的产量估算中的潜在应用。
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