This paper proposes a novel approach for living and missing vine identification and vine characterization in goblet-trained vine plots using aerial images. Given the periodic structure of goblet vineyards, the RGB color coded parcel image is analyzed using proper processing techniques in order to determine the locations of living and missing vines. Vine characterization is achieved by implementing the marker-controlled watershed transform where the centers of the living vines serve as object markers. As a result, a precise mortality rate is calculated for each parcel. Moreover, all vines, even the overlapping ones, are fully recognized providing information about their size, shape, and green color intensity. The presented approach is fully automated and yields accuracy values exceeding 95% when the obtained results are assessed with ground-truth data. This unsupervised and automated approach can be applied to any type of plots presenting similar spatial patterns requiring only the image as input.

中文翻译：

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使用遥感图像对经过高脚杯训练的葡萄园的葡萄藤进行识别和表征

本文提出了一种使用航拍图像在经过高脚杯训练的葡萄树地块中识别活葡萄树和失踪葡萄树以及表征葡萄树的新方法。考虑到高脚杯葡萄园的周期性结构，使用适当的处理技术分析 RGB 颜色编码的地块图像，以确定存活和缺失葡萄藤的位置。葡萄树特征是通过实施标记控制的分水岭变换来实现的，其中活葡萄树的中心作为对象标记。因此，计算出每个地块的精确死亡率。此外，所有葡萄藤，甚至是重叠的葡萄藤，都可以被完全识别，提供有关其大小、形状和绿色强度的信息。当使用真实数据评估获得的结果时，所提出的方法是完全自动化的，并且产生超过 95% 的准确度值。这种无监督和自动化的方法可以应用于任何类型的显示类似空间模式的图，只需要图像作为输入。