# Multi-resolution Outlier Pooling for Sorghum Classification

arXiv: https://arxiv.org/abs/2106.05748 · HTML: https://mlsanity.com/paper/2106.05748/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 10 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Scene recognition with CNNs: objects, scales and dataset bias](https://mlsanity.com/paper/1801.06867/) (2018)
- [Estimating Phenotypic Traits From UAV Based RGB Imagery](https://mlsanity.com/paper/1807.00498/) (2018)

## Built on by (most cited first, 3 of 3)

- [Self-Supervised Backbone Framework for Diverse Agricultural Vision Tasks](https://mlsanity.com/paper/2403.15248/) (2024)
- [What Does TERRA-REF's High Resolution, Multi Sensor Plant Sensing Public Domain Data Offer the Computer Vision Community?](https://mlsanity.com/paper/2107.14072/) (2021)
- [Classification and Visualization of Genotype x Phenotype Interactions in Biomass Sorghum](https://mlsanity.com/paper/2108.04090/) (2021)

## Abstract (arXiv)

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.