The advance of digital agriculture combined with computational tools and Unmanned Aerial Vehicles (UAVs) has enabled the collection of data for reliably extracting vegetation indices and biophysical parameters derived from the Structure from Motion (SfM) algorithm. This work aimed to evaluate the accuracy of the photogrammetry technique using an SfM point cloud for the estimation of the height (h) and crown diameter (d) of coffee trees from aerial images obtained by UAV with an RGB (Red, Green, Blue) camera and compared the results with data measured in situ for 12 months. The experiment was carried out in a coffee plantation, Lavras, Minas Gerais, Brazil. A rotary-wing UAV was used in autonomous flight mode and coupled to a conventional camera, flying at a height of 30 m with an image overlap of 80% and a speed of 3 m/s. The images were processed using PhotoScan software, and the analyses were performed in Qgis. A correlation of 87% was obtained between the h values in the field and h values obtained by the UAV, and there was a 95% correlation between the d values obtained in the field and the values obtained by the UAV. It was possible to obtain significant estimates of the attributes, such as the h and d of coffee trees, using UAV–SfM images acquired with an RGB digital camera.

中文翻译：

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无人机RGB图像估计的咖啡作物生物物理参数

数字农业的进步与计算工具和无人驾驶飞行器 (UAV) 相结合，能够收集数据以可靠地提取从运动结构 (SfM) 算法得出的植被指数和生物物理参数。这项工作旨在评估使用 SfM 点云的摄影测量技术的准确性，以从无人机获得的航空图像中估计咖啡树的高度 (h) 和树冠直径 (d)（红色、绿色、蓝色）相机并将结果与​​原位测量的数据进行了 12 个月的比较。实验在巴西米纳斯吉拉斯州拉夫拉斯的咖啡种植园进行。旋翼无人机采用自主飞行模式，与传统相机耦合，飞行高度为30 m，图像重叠率为80%，速度为3 m/s。使用 PhotoScan 软件处理图像，并在 Qgis 中进行分析。野外得到的 h 值与无人机得到的 h 值之间的相关性为 87%，野外得到的 d 值与无人机得到的值之间的相关性为 95%。使用 RGB 数码相机获取的 UAV-SfM 图像可以获得对属性的重要估计，例如咖啡树的 h 和 d。