The knowledge of vine vegetative status within a vineyard plays a key role in canopy management in order to achieve a correct vine balance and reach the final desired yield/quality. Detailed information about canopy architecture and missing plants distribution provides useful support for farmers/winegrowers to optimize canopy management practices and the replanting process, respectively. In the last decade, there has been a progressive diffusion of UAV (Unmanned Aerial Vehicles) technologies for Precision Viticulture purposes, as fast and accurate methodologies for spatial variability of geometric plant parameters. The aim of this study was to implement an unsupervised and integrated procedure of biomass estimation and missing plants detection, using both the 2.5D-surface and 3D-alphashape methods. Both methods showed good overall accuracy respect to ground truth biomass measurements with high values of R2 (0.71 and 0.80 for 2.5D and 3D, respectively). The 2.5D method led to an overestimation since it is derived by considering the vine as rectangular cuboid form. On the contrary, the 3D method provided more accurate results as a consequence of the alphashape algorithm, which is capable to detect each single shoot and holes within the canopy. Regarding the missing plants detection, the 3D approach confirmed better performance in cases of hidden conditions by shoots of adjacent plants or sparse canopy with some empty spaces along the row, where the 2.5D method based on the length of section of the row with lower thickness than the threshold used (0.10 m), tended to return false negatives and false positives, respectively. This paper describes a rapid and objective tool for the farmer to promptly identify canopy management strategies and drive replanting decisions. The 3D approach provided results closer to real canopy volume and higher performance in missing plant detection. However, the dense cloud based analysis required more processing time. In a future perspective, given the continuous technological evolution in terms of computing performance, the overcoming of the current limit represented by the pre- and post-processing phases of the large image dataset should mainstream this methodology.

中文翻译：

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基于 2.5D 和 3D 方法使用无人机平台获取的 RGB 图像评估用于树冠生物量估计和缺失植物检测的新型精密葡萄栽培工具。

了解葡萄园内葡萄藤的营养状况在树冠管理中起着关键作用，以实现正确的葡萄藤平衡并达到最终所需的产量/质量。有关树冠结构和缺失植物分布的详细信息为农民/葡萄种植者分别优化树冠管理实践和补种过程提供了有用的支持。在过去十年中，用于精确葡萄栽培目的的 UAV（无人驾驶飞行器）技术逐渐普及，作为几何植物参数空间可变性的快速准确方法。本研究的目的是使用 2.5D 表面和 3D-alphashape 方法实施生物量估计和缺失植物检测的无监督和集成程序。两种方法在地面实况生物量测量方面均显示出良好的整体准确度，R2 值较高（2.5D 和 3D 分别为 0.71 和 0.80）。2.5D 方法导致了高估，因为它是通过将藤蔓视为长方体形式得出的。相反，由于 alphashape 算法，3D 方法提供了更准确的结果，该算法能够检测到树冠内的每个单芽和孔洞。在缺失植物检测方面，3D 方法在相邻植物的枝条或沿行有一些空白的稀疏冠层的隐藏条件下证实了更好的性能，其中 2.5D 方法基于较低厚度的行截面长度比使用的阈值（0.10 m），往往会分别返回假阴性和假阳性。本文介绍了一种快速、客观的工具，可帮助农民迅速确定树冠管理策略并推动重新种植决策。3D 方法提供了更接近真实冠层体积的结果，并且在缺失植物检测方面具有更高的性能。然而，基于密集云的分析需要更多的处理时间。从未来的角度来看，鉴于计算性能方面的技术不断发展，克服以大图像数据集的预处理和后处理阶段为代表的当前限制应该成为该方法的主流。基于密集云的分析需要更多的处理时间。从未来的角度来看，鉴于计算性能方面的技术不断发展，克服以大图像数据集的预处理和后处理阶段为代表的当前限制应该成为该方法的主流。基于密集云的分析需要更多的处理时间。从未来的角度来看，鉴于计算性能方面的技术不断发展，克服以大图像数据集的预处理和后处理阶段为代表的当前限制应该成为该方法的主流。