Background Almond is an emerging crop due to the health benefits of almond consumption including nutritional, anti-inflammatory, and hypocholesterolaemia properties. Traditional almond producers were concentrated in California, Australia, and Mediterranean countries. However, almond is currently present in more than 50 countries due to breeding programs have modernized almond orchards by developing new varieties with improved traits related to late flowering (to reduce the risk of damage caused by late frosts) and tree architecture. Almond tree architecture and flowering are acquired and evaluated through intensive field labour for breeders. Flowering detection has traditionally been a very challenging objective. To our knowledge, there is no published information about monitoring of the tree flowering dynamics of a crop at the field scale by using color information from photogrammetric 3D point clouds and OBIA. As an alternative, a procedure based on the generation of colored photogrammetric point clouds using a low cost (RGB) camera on-board an unmanned aerial vehicle (UAV), and an semi-automatic object based image analysis (OBIA) algorithm was created for monitoring the flower density and flowering period of every almond tree in the framework of two almond phenotypic trials with different planting dates. Results Our method was useful for detecting the phenotypic variability of every almond variety by mapping and quantifying every tree height and volume as well as the flowering dynamics and flower density. There was a high level of agreement among the tree height, flower density, and blooming calendar derived from our procedure on both fields with the ones created from on-ground measured data. Some of the almond varieties showed a significant linear fit between its crown volume and their yield. Conclusions Our findings could help breeders and researchers to reduce the gap between phenomics and genomics by generating accurate almond tree information in an efficient, non-destructive, and inexpensive way. The method described is also useful for data mining to select the most promising accessions, making it possible to assess specific multi-criteria ranking varieties, which are one of the main tools for breeders.

中文翻译：

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一种高效的基于 RGB-UAV 的田间杏仁树表型分析平台：3-D 结构和开花性状。

背景 杏仁是一种新兴作物，因为食用杏仁对健康有益，包括营养、抗炎和低胆固醇血症特性。传统的杏仁生产商集中在加利福尼亚、澳大利亚和地中海国家。然而，杏仁目前存在于 50 多个国家，因为育种计划通过开发具有与晚开花（以减少晚霜造成的损害风险）和树结构相关的改良性状的新品种，使杏仁园现代化。杏仁树的结构和开花是通过繁育者的密集田间劳动获得和评估的。开花检测传统上一直是一个非常具有挑战性的目标。据我们所知，没有关于使用摄影测量 3D 点云和 OBIA 的颜色信息在田间尺度上监测作物树木开花动态的公开信息。作为替代方案，基于使用无人驾驶飞行器 (UAV) 上的低成本 (RGB) 相机生成彩色摄影测量点云的程序和基于对象的半自动图像分析 (OBIA) 算法被创建用于在两个不同种植日期的杏仁表型试验框架内监测每棵杏仁树的花密度和开花期。结果 我们的方法可用于通过绘制和量化每棵树的高度和体积以及开花动态和花密度来检测每个杏仁品种的表型变异性。树高之间有高度的一致性，花密度和开花日历来自我们在两个领域的程序以及根据地面测量数据创建的那些。一些杏仁品种在其树冠体积和产量之间表现出显着的线性拟合。结论 我们的研究结果可以通过以高效、非破坏性和廉价的方式生成准确的杏仁树信息来帮助育种者和研究人员缩小表型组学和基因组学之间的差距。所描述的方法也可用于数据挖掘以选择最有希望的种质，从而可以评估特定的多标准排序品种，这是育种者的主要工具之一。一些杏仁品种在其树冠体积和产量之间表现出显着的线性拟合。结论 我们的研究结果可以通过以高效、非破坏性和廉价的方式生成准确的杏仁树信息来帮助育种者和研究人员缩小表型组学和基因组学之间的差距。所描述的方法也可用于数据挖掘以选择最有希望的种质，从而可以评估特定的多标准排序品种，这是育种者的主要工具之一。一些杏仁品种在其树冠体积和产量之间表现出显着的线性拟合。结论 我们的研究结果可以通过以高效、非破坏性和廉价的方式生成准确的杏仁树信息来帮助育种者和研究人员缩小表型组学和基因组学之间的差距。所描述的方法也可用于数据挖掘以选择最有希望的种质，从而可以评估特定的多标准排序品种，这是育种者的主要工具之一。