Application of 2D and 3D image technologies to characterise morphological attributes of grapevine clusters
Application of 2D and 3D image technologies to characterise morphological attributes of grapevine clusters
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DOI：
10.1002/jsfa.7675
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发表时间：
2016-10-01
影响因子：
4.1
通讯作者：
Ibanez, Javier
中科院分区：
文献类型：
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作者：
Tello, Javier;Cubero, Sergio;Ibanez, Javier
背景：葡萄果穗形态影响着葡萄酒和鲜食葡萄的品质和商业价值。它通常是通过不符合食品行业要求的主观和不准确的方法进行评估的。结果：通过对2D图像的分析，成功地实现了簇的长度、宽度和伸长率的自动评估，与人工方法的相关系数分别为0.959、0.861和0.852。根据形状对簇进行分类可以通过评估簇的不同部分中的锥度来实现。基于2D特征的簇形态体积几何重建效果优于直接3D激光扫描系统，与手动方法(水位移法)相关性较高(r=0.956)。此外，我们还构建并验证了一个简单的线性回归模型来估计集群的紧凑度。该方法对聚类的训练子集和验证子集都具有较高的预测能力(R2分别为84.5%和71.1%)。结论：本文提出的方法为快速、客观地表征聚类形态提供了连续、准确的数据。(三)2016年化学工业学会
BACKGROUND: Grapevine cluster morphology influences the quality and commercial value of wine and table grapes. It is routinely evaluated by subjective and inaccurate methods that do not meet the requirements set by the food industry. Novel two-dimensional (2D) and three-dimensional (3D) machine vision technologies emerge as promising tools for its automatic and fast evaluation.RESULTS: The automatic evaluation of cluster length, width and elongation was successfully achieved by the analysis of 2D images, significant and strong correlations with the manual methods being found (r = 0.959, 0.861 and 0.852, respectively). The classification of clusters according to their shape can be achieved by evaluating their conicity in different sections of the cluster. The geometric reconstruction of the morphological volume of the cluster from 2D features worked better than the direct 3D laser scanning system, showing a high correlation (r = 0.956) with the manual approach (water displacement method). In addition, we constructed and validated a simple linear regression model for cluster compactness estimation. It showed a high predictive capacity for both the training and validation subsets of clusters (R-2 = 84.5 and 71.1%, respectively).CONCLUSION: The methodologies proposed in this work provide continuous and accurate data for the fast and objective characterisation of cluster morphology. (C) 2016 Society of Chemical Industry