The optimisation of vineyards management requires efficient and automated methods able to identify individual plants. In the last few years, Unmanned Aerial Vehicles (UAVs) have become one of the main sources of remote sensing information for Precision Viticulture (PV) applications. In fact, high resolution UAV-based imagery offers a unique capability for modelling plant’s structure making possible the recognition of significant geometrical features in photogrammetric point clouds. Despite the proliferation of innovative technologies in viticulture, the identification of individual grapevines relies on image-based segmentation techniques. In that way, grapevine and non-grapevine features are separated and individual plants are estimated usually considering a fixed distance between them. In this study, an automatic method for grapevine trunk detection, using 3D point cloud data, is presented. The proposed method focuses on the recognition of key geometrical parameters to ensure the existence of every plant in the 3D model. The method was tested in different commercial vineyards and to push it to its limit a vineyard characterised by several missing plants along the vine rows, irregular distances between plants and occluded trunks by dense vegetation in some areas, was also used. The proposed method represents a disruption in relation to the state of the art, and is able to identify individual trunks, posts and missing plants based on the interpretation and analysis of a 3D point cloud. Moreover, a validation process was carried out allowing concluding that the method has a high performance, especially when it is applied to 3D point clouds generated in phases in which the leaves are not yet very dense (January to May). However, if correct flight parametrizations are set, the method remains effective throughout the entire vegetative cycle.

中文翻译：

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基于无人机的点云上的葡萄干自动检测

葡萄园管理的优化需要能够识别单个植物的高效自动化方法。在过去的几年中，无人飞行器（UAV）已成为精确葡萄栽培（PV）应用的主要遥感信息来源之一。实际上，基于高分辨率无人机的影像为建模工厂结构提供了独特的功能，从而使人们能够识别摄影测量点云中的重要几何特征。尽管葡萄栽培中的创新技术迅速普及，但单个葡萄藤的鉴定仍依赖于基于图像的分割技术。这样，葡萄和非葡萄的特征就被分离了，估计单个植物通常考虑它们之间的固定距离。在这个研究中，提出了一种利用3D点云数据自动检测葡萄干的方法。所提出的方法侧重于关键几何参数的识别，以确保3D模型中每个植物的存在。该方法在不同的商业葡萄园中进行了测试，并将其推向极限，该葡萄园的特征是沿葡萄藤行缺少几株植物，某些地区的植物之间的距离不规则，并且由于茂密的植被而堵塞了树干。所提出的方法代表了相对于现有技术的破坏，并且能够基于3D点云的解释和分析来识别单个树干，柱子和丢失的植物。此外，进行了验证过程，可以得出结论该方法具有较高的性能，尤其是当将其应用于3D点云时，该阶段在叶子还不太密集的阶段（1月至5月）生成。但是，如果设置了正确的飞行参数，则该方法在整个营养循环中仍然有效。