Automated 3D reconstruction of grape cluster architecture from sensor data for efficient phenotyping
Automated 3D reconstruction of grape cluster architecture from sensor data for efficient phenotyping
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DOI：
10.1016/j.compag.2015.04.001
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发表时间：
2015-06-01
影响因子：
8.3
通讯作者：
Steinhage, Volker
中科院分区：
文献类型：
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作者：
Schoeler, Florian;Steinhage, Volker
我们提出了一种对葡萄簇结构进行全自动、基于传感器的 3D 重建的方法，然后对表型性状进行精确、客观和可重复的推导。当前基于传感器的表型分析方法通常显示交互式处理步骤，并仅分析植物中可以被给定传感器系统感知的那些部分。我们的方法采用了基于显式组件的葡萄簇架构模型，即葡萄簇组件的互连性、组件的几何形状以及它们相互连接的结构和几何约束。基于该模型，我们的方法可以以完全自动化的方式对感测到的葡萄串进行完整的 3D 重建，即使在传感器数据采集过程中出现部分遮挡的情况下也是如此。给定葡萄串的完整 3D 重建，我们一方面可以得出葡萄串众所周知的表型特征。另一方面，这种方法有助于测量和评估新的表型性状。因此，我们的方法对于葡萄园以及葡萄育种者的监测和产量估算很有意义。我们在一个小道消息表型项目中开发并实施了我们的方法。对重建结果和衍生表型性状的首次评估显示了这种方法用于自动化高通量表型分析的潜力。我们讨论将我们的方法应用于其他工厂和其他传感器系统的机会。 (C) 2015 Elsevier B.V. 保留所有权利。
We propose an approach to fully-automated and sensor-based 3D reconstruction of grape cluster architecture followed by a precise, objective, and reproducible derivation of phenotypic traits. Current approaches to sensor-based phenotyping often show interactive processing steps and analyze only those parts of a plant that can be sensed by the given sensor system. Our approach employs an explicit component-based model of the architecture of grape clusters, i.e., the interconnectivity of a grape cluster's components, the geometry of the components, and the structural and geometrical constraints of their mutual connections. Based on this model, our approach can derive in a fully automated way complete 3D reconstructions of sensed grape clusters even for cases of partial occlusions in the process of sensor data acquisition. Given a complete 3D reconstruction of a grape cluster, we can derive on the one hand well known phenotypic traits of grape clusters. On the other hand, this approach facilitates measuring and evaluating new phenotypic traits. Therefore, our approach is of interest for monitoring and yield estimations in vineyards as well as for grapevine breeders. We developed and implemented our approach within a grapevine phenotyping project. First evaluations of reconstruction results and derived phenotypic traits show a potential of this approach for automated high-throughput phenotyping. We discuss the opportunities to apply our approach to other plants and with other sensor systems. (C) 2015 Elsevier B.V. All rights reserved.