Multi-resolution Outlier Pooling for Sorghum Classification
This work addresses the problem of low inter-class variance in automated plant phenotyping for crop breeding programs, representing an incremental improvement in domain-specific classification.
The paper tackles the challenging visual recognition task of classifying highly related sorghum cultivars using RGB imagery, introducing a new global pooling strategy called Dynamic Outlier Pooling that outperforms standard methods on the Sorghum-100 dataset.
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.