Semi-Automated Field Plot Segmentation From UAS Imagery for Experimental Agriculture.
Semi-Automated Field Plot Segmentation From UAS Imagery for Experimental Agriculture.
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DOI：
10.3389/fpls.2020.591886
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发表时间：
2020
影响因子：
5.6
通讯作者：
Brook J
中科院分区：
文献类型：
--
作者：
Robb C;Hardy A;Doonan JH;Brook J
提出了一种从无人机图像中准确分割作物地块的图像处理方法。无人机用于农业监测的使用显著增加，成为一种潜在的成本效益替代载人航空调查和远程评估作物状态的野外工作。从广泛地区的无人机图像中准确分割出密集种植的小块作物地块是这一监测活动的重要组成部分，以便及时和经济有效地评估不同品种和处理制度的状况。尽管它的重要性，一个可靠的作物地块分割方法逃避我们，最好的努力是依靠显著的手动参数化。所开发的分割方法使用边缘检测和霍夫线检测相结合的方法来建立每个地块的边界，并为每个地块段计算基于像素/点的度量。我们表明，在有限的参数化条件下，在不同的作物类型和条件下，作物地块的分割精度可以保持在89%以上。这与从稻田中获得的结果相当，在稻田中，小块的植物材料与水形成鲜明对比，并且比以前用于典型旱地作物的方法有很大的改进。
We present an image processing method for accurately segmenting crop plots from Unmanned Aerial System imagery (UAS). The use of UAS for agricultural monitoring has increased significantly, emerging as a potentially cost effective alternative to manned aerial surveys and field work for remotely assessing crop state. The accurate segmentation of small densely-packed crop plots from UAS imagery over extensive areas is an important component of this monitoring activity in order to assess the state of different varieties and treatment regimes in a timely and cost-effective manner. Despite its importance, a reliable crop plot segmentation approach eludes us, with best efforts being relying on significant manual parameterization. The segmentation method developed uses a combination of edge detection and Hough line detection to establish the boundaries of each plot with pixel/point based metrics calculated for each plot segment. We show that with limited parameterization, segmentation of crop plots consistently over 89% accuracy are possible on different crop types and conditions. This is comparable to results obtained from rice paddies where the plant material in plots is sharply contrasted with the water, and represents a considerable improvement over previous methods for typical dry land crops.
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DOI：
10.3390/s8053557
发表时间：
2008-05-26
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Lelong CC;Burger P;Jubelin G;Roux B;Labbé S;Baret F
通讯作者：
Baret F
影响因子：
10.6
作者：
Chan, TF;Vese, LA
通讯作者：
Vese, LA
影响因子：
3.2
作者：
Mavridou E;Vrochidou E;Papakostas GA;Pachidis T;Kaburlasos VG
通讯作者：
Kaburlasos VG
影响因子：
8.3
作者：
Bai, X. D.;Cao, Z. G.;Li, C. N.
通讯作者：
Li, C. N.
影响因子：
6.2
作者：
Chen, Ruizhi;Chu, Tianxing;Maeda, Murilo M.
通讯作者：
Maeda, Murilo M.