In-field crop row phenotyping from 3D modeling performed using Structure from Motion
In-field crop row phenotyping from 3D modeling performed using Structure from Motion
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DOI：
10.1016/j.compag.2014.09.021
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发表时间：
2015-01-01
影响因子：
8.3
通讯作者：
Gorretta, Nathalie
中科院分区：
文献类型：
--
作者：
Jay, Sylvain;Rabatel, Gilles;Gorretta, Nathalie
本文提出了一种适用于表型相关问题的作物行结构表征方法。在所提出的方法中，作物行的三维模型的建立，并作为一个基础上检索植物结构参数。该模型是使用“运动结构”计算的，其中RGB图像是通过沿着行平移单个相机获取的。然后，估计植物的高度和叶面积，植物和背景区分的鲁棒的方法，使用颜色和高度的信息，以处理低对比度的区域。三维模型的缩放和植物表面最终近似使用三角形网格。我们的方法的有效性进行了评估，在室外条件下收集的两个数据集。我们还评估了其对各种植物结构，传感器，采集技术和光照条件的鲁棒性。作物行3D模型是准确的，并导致令人满意的高度估计结果，因为平均误差和参考测量误差是相似的。叶面积估计也获得了较强的相关性和较低的误差。由于其易于使用，估计精度和户外条件下的鲁棒性，我们的方法提供了一个操作工具的表型应用。(C)2014爱思唯尔有限公司版权所有。
This article presents a method for crop row structure characterization that is adapted to phenotyping-related issues. In the proposed method, a crop row 3D model is built and serves as a basis for retrieving plant structural parameters. This model is computed using Structure from Motion with RGB images acquired by translating a single camera along the row. Then, to estimate plant height and leaf area, plant and background are discriminated by a robust method that uses both color and height information in order to handle low-contrasted regions. The 3D model is scaled and the plant surface is finally approximated using a triangular mesh.The efficacy of our method was assessed with two data sets collected under outdoor conditions. We also evaluated its robustness against various plant structures, sensors, acquisition techniques and lighting conditions. The crop row 3D models were accurate and led to satisfactory height estimation results, since both the average error and reference measurement error were similar. Strong correlations and low errors were also obtained for leaf area estimation. Thanks to its ease of use, estimation accuracy and robustness under outdoor conditions, our method provides an operational tool for phenotyping applications. (C) 2014 Elsevier B.V. All rights reserved.