Multi-resolution Outlier Pooling for Sorghum Classification
- URL: http://arxiv.org/abs/2106.05748v1
- Date: Thu, 10 Jun 2021 13:57:33 GMT
- Title: Multi-resolution Outlier Pooling for Sorghum Classification
- Authors: Chao Ren, Justin Dulay, Gregory Rolwes, Duke Pauli, Nadia Shakoor and
Abby Stylianou
- Abstract summary: We introduce the Sorghum-100 dataset, a large dataset of RGB imagery of sorghum captured by a state-of-the-art gantry system.
A new global pooling strategy called Dynamic Outlier Pooling outperforms standard global pooling strategies on this task.
- Score: 4.434302808728865
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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