Automated high throughput plant phenotyping involves leveraging sensors, such as RGB, thermal and hyperspectral cameras (among others), to make large scale and rapid measurements of the physical properties of plants for the purpose of better understanding the difference between crops and facilitating rapid plant breeding programs. One of the most basic phenotyping tasks is to determine the cultivar, or species, in a particular sensor product. This simple phenotype can be used to detect errors in planting and to learn the most differentiating features between cultivars. It is also a challenging visual recognition task, as a large number of highly related crops are grown simultaneously, leading to a classification problem with low inter-class variance. In this paper, we introduce the Sorghum-100 dataset, a large dataset of RGB imagery of sorghum captured by a state-of-the-art gantry system, a multi-resolution network architecture that learns both global and fine-grained features on the crops, and a new global pooling strategy called Dynamic Outlier Pooling which outperforms standard global pooling strategies on this task.

翻译：自动化高排量植物口交包括利用传感器,如RGB、热光谱和超光谱照相机(等等),对植物的物理特性进行大规模和快速测量,以便更好地了解作物之间的差别并促进快速的植物育种程序。最基本的口交任务之一是确定某个特定传感产品中的栽种或物种。这种简单的苯型可以用来探测栽种中的错误和了解栽种中最不同的特征。它也是一项具有挑战性的视觉识别任务,因为大量高度相关的作物同时生长,导致一个分类问题,造成低等级差异。在本文中,我们介绍了Sorghum-100数据集,一个由最先进的导管系统捕捉到的orghum成像的大型RGB数据集,一个多分辨率网络结构,它既了解作物的全球特征,又精细的特征,以及一个称为动态外源集合的新的全球联合战略,它比这项任务的标准全球聚合战略要强得多。