Jerbi Taha, Wuyts Nathalie, Cane Maria Angela, Faux Philippe-François, Draye Xavier
Earth and Life Institute, Université catholique de Louvain, Croix du Sud 2 L7.05.11, 1348 Louvain-la-Neuve, Belgium.
Di.S.T.A. Department of Agroenvironmental Sciences and Technologies, University of Bologna, Viale Fanin 44, 40127 Bologna, Italy.
Funct Plant Biol. 2015 Sep;42(9):858-864. doi: 10.1071/FP15024.
The use of remote sensors (thermometers and cameras) to analyse crop water status in field conditions is fraught with several difficulties. In particular, average canopy temperature measurements are affected by the mixture of soil and green regions, the mutual shading of leaves and the variability of absorbed radiation. The aim of the study was to analyse how the selection of different 'regions of interest' (ROI) in canopy images affect the variability of the resulting temperature averages. Using automated image segmentation techniques we computed the average temperature in four nested ROI of decreasing size, from the whole image down to the sunlit fraction of a leaf located in the upper part of the canopy. The study was conducted on maize (Zea mays L.) at the flowering stage, for its large leaves and well structured canopy. Our results suggest that, under these conditions, the ROI comprising the sunlit fraction of a leaf located in the upper part of the canopy should be analogous to the single leaf approach (in controlled conditions) that allows the estimation of stomatal conductance or plant water potential.
在田间条件下使用远程传感器（温度计和摄像头）分析作物水分状况存在诸多困难。特别是，冠层平均温度测量会受到土壤和绿色区域混合、叶片相互遮荫以及吸收辐射变异性的影响。本研究的目的是分析冠层图像中不同“感兴趣区域”（ROI）的选择如何影响所得温度平均值的变异性。我们使用自动图像分割技术计算了四个大小递减的嵌套ROI中的平均温度，从整个图像到冠层上部一片叶子的受光部分。该研究以处于开花期的玉米（Zea mays L.）为对象，因其叶片大且冠层结构良好。我们的结果表明，在这些条件下，包含冠层上部一片叶子受光部分的ROI应类似于（在可控条件下）允许估算气孔导度或植物水势的单叶方法。
