ABSTRACT

Thermal stress indicators are one of the most accurate indices for sensing plant water status that can be remotely measured by the means of infrared thermography. In addition to the canopy temperature, these indices need to access the wet and dry reference temperatures which refer to the temperatures of the canopy at well-watered and fully stressed conditions, respectively. The main goal of this study is to measure the canopy as well as reference temperatures automatically by the means of a single thermal image, captured from an olive tree. The temperatures of artificial reference surfaces were extracted by the means of an object detection method based on the edge detection and morphological processes. The temperatures of sunlit and shaded canopy portions were also detected, using a Fuzzy C-means clustering of thermal images with the wet and dry reference temperatures as thresholds. The algorithm was successfully detected the references in 90% of the images and the automatic extracted canopy temperatures were significantly correlated with the manual ones.

摘要

热应力指标是传感植物水分状况的最准确指标之一，可以通过红外热成像进行远程测量。除了冠层温度之外，这些指标还需要获取湿和干参考温度，它们分别是指在充分浇水和完全受压条件下的冠层温度。本研究的主要目标是通过从橄榄树捕获的单个热图像自动测量树冠和参考温度。通过基于边缘检测和形态学过程的目标检测方法提取人工参考表面的温度。还检测了阳光照射和树冠阴影部分的温度，使用干湿参考温度作为阈值的模糊 C 均值聚类热图像。该算法成功检测到90%的图像中的参考，并且自动提取的冠层温度与手动提取的温度显着相关。