Background and Aims Canopy assessment of the fruiting zone can lead to more informed vineyard management decisions. A non‐destructive, image‐based system capable of operating on‐the‐go was developed to assess canopy porosity, and leaf and bunch exposure of red grape cultivars in the vineyard. Methods and Results On‐the‐go (7 km/h) night time images of a vertically shoot positioned commercial vineyard canopy were acquired with an automated red green blue imaging system, coupled to a GPS and controlled artificial lighting. The reference method was point quadrat analysis. Sound correlations between the image analysis and point quadrat analysis results for the proportion of gaps (R2 > 0.85; P 0.57; P < 0.001) were obtained for both sides of the canopy. For the bunch to canopy area ratio the best relationship was found on the western side of the canopy (R2 = 0.79; P < 0.001). Also maps of the three canopy variables were built in a commercial vineyard to compare their spatial variability on the east and west sides across the whole vineyard plot. Conclusions The developed imaging system, capable of operating on‐the‐go, can yield quantitative, objective and reliable knowledge of what a grapegrower would assess by subjective, qualitative visual inspection of the grapevine canopy. The information can be used to help make better informed decisions about leaf removal, and if mapped may help to delineate zones amenable to homogeneous management. Significance of the Study The new developed computer vision system can be mounted on any vehicle, such as a tractor, all terrain vehicle and robot, for a rapid and objective monitoring of the vineyard canopy around the fruiting zone in red cultivars and vertically shoot positioned trained vines. Moreover, the maps generated could be used by a new generation of variable rate viticultural machinery to spatially optimise vineyard cultural practices.

中文翻译：

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通过图像分析实时评估葡萄园冠层孔隙度、束和叶片暴露

背景和目标 果区的冠层评估可以导致更明智的葡萄园管理决策。开发了一种能够在旅途中运行的非破坏性、基于图像的系统，用于评估葡萄园中红葡萄品种的冠层孔隙率、叶片和葡萄串暴露情况。方法和结果 垂直拍摄定位的商业葡萄园树冠的行驶中（7 公里/小时）夜间图像是通过自动红绿蓝成像系统与 GPS 和受控人工照明耦合获得的。参考方法是点样方分析。对于冠层两侧的间隙比例（R2 > 0.85; P < 0.57; P < 0.001），图像分析和点样方分析结果之间的声音相关性被获得。对于束与冠层面积比，在冠层西侧发现了最佳关系（R2 = 0.79；P < 0.001）。此外，在商业葡萄园中构建了三个冠层变量的地图，以比较整个葡萄园地块东侧和西侧的空间变异性。结论 开发的成像系统能够在旅途中运行，可以定量、客观和可靠地了解葡萄种植者通过对葡萄树冠层进行主观、定性目视检查的评估。该信息可用于帮助做出关于叶片去除的更明智的决定，如果绘制地图可能有助于划定适合均质管理的区域。研究意义 新开发的计算机视觉系统可以安装在任何车辆上，如拖拉机、全地形车和机器人，用于快速客观地监测红色栽培品种和垂直拍摄定位的受过训练的葡萄藤的结果区周围的葡萄园冠层。此外，生成的地图可用于新一代可变速率葡萄栽培机械，以在空间上优化葡萄园文化实践。