Multi-resolution Outlier Pooling for Sorghum Classification

TL;DR

This paper introduces a new dataset, a multi-resolution network architecture, and a novel pooling strategy called Dynamic Outlier Pooling for improved sorghum cultivar classification from RGB images.

Contribution

It presents the Sorghum-100 dataset, a multi-resolution network, and the Dynamic Outlier Pooling method, advancing plant phenotyping accuracy.

Findings

Dynamic Outlier Pooling outperforms standard pooling methods

The multi-resolution network captures both global and fine-grained features

The dataset enables large-scale sorghum classification research

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.