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Comparison of two different measurement techniques for automated determination of plum tree canopy cover.

机译:自动确定李子树冠覆盖的两种不同测量技术的比较。

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The transnational project "3D Mosaic" deals with the optimisation of water and fertiliser efficiency in orchards. Detection of the canopy coverage at tree level provides information about the growth capacity of the tree and enables estimation of the possible yield or the influence of reduced water supply in an orchard. Detection must be performed in an automated mode that may be achieved by means of two optical approaches: NIR image analysis, with the calculation of leaf coverage within the image versus non-covered area, and counting the number of laser-scanner (LiDAR) hits per tree. The present study, conducted in an experimental orchard of 180 plum trees, aimed to evaluate and compare these methods using a vertical top-down viewing direction for the sensors. Image analysis showed a higher susceptibility to the sensor mounting height and tilting movements of the carrier vehicle than did the LiDAR measurements. However, on uniform terrain, a Pearson correlation of 0.917 between the systems could be achieved. Both techniques were compared with the manually counted number of leaves per tree for the entire orchard and with the estimated total leaf area for 30 strategically distributed trees. Due to different shapes of the tree crown, the comparison with the leaf numbers yielded lower Pearson correlations for the pollinator cultivar (0.703 with LiDAR, 0.668 with camera) than for the productive trees (0.805 with LiDAR, 0.832 with camera). Comparison of the sensors with the estimated leaf areas yielded correlation coefficients of 0.867 with the laser scanner and 0.788 with image analysis.Digital Object Identifier http://dx.doi.org/10.1016/j.biosystemseng.2012.09.014
机译:跨国项目“ 3D马赛克”致力于果园中水和肥料效率的优化。在树级别检测树冠覆盖范围可提供有关树的生长能力的信息,并能够估算果园中可能的产量或减少的供水量的影响。必须以自动方式执行检测,这可以通过两种光学方法来实现:NIR图像分析,计算图像相对未覆盖区域的叶片覆盖率,并计算激光扫描仪(LiDAR)命中的次数每棵树。本研究在180株李子树的试验果园中进行,旨在使用传感器的垂直自顶向下观察方向评估和比较这些方法。图像分析显示,与LiDAR测量相比,对运载工具的传感器安装高度和倾斜运动的敏感性更高。但是,在均匀地形上,系统之间的Pearson相关性可以达到0.917。将这两种技术与人工计算的整个果园中每棵树的叶子数量以及估计的30棵有战略意义的树木的总叶子面积进行了比较。由于树冠的形状不同,与传粉品种的比较(与LiDAR的0.703,与摄像头的0.668),与授粉品种的叶数比较,产生的皮尔逊相关性比生产性树木(LiDAR的0.805,摄像头的0.832)低。传感器与估计叶面积的比较得出的相关系数在激光扫描仪中为0.867,在图像分析中为0.788。数字对象标识符http://dx.doi.org/10.1016/j.biosystemseng.2012.09.014

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