# Classification and Visualization of Genotype x Phenotype Interactions in Biomass Sorghum

arXiv: https://arxiv.org/abs/2108.04090 · HTML: https://mlsanity.com/paper/2108.04090/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 47 references that are in the map.
- First posted on arXiv 9 Aug 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Using Deep Learning for Image-Based Plant Disease Detection](https://mlsanity.com/paper/1604.03169/) (2016)
- [Deep Fruit Detection in Orchards](https://mlsanity.com/paper/1610.03677/) (2016)
- [Leaf Counting with Deep Convolutional and Deconvolutional Networks](https://mlsanity.com/paper/1708.07570/) (2017)
- [Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection](https://mlsanity.com/paper/2106.07770/) (2021)
- [Leveraging multiple datasets for deep leaf counting](https://mlsanity.com/paper/1709.01472/) (2017)
- [Multi-resolution Outlier Pooling for Sorghum Classification](https://mlsanity.com/paper/2106.05748/) (2021)

## Abstract (arXiv)

We introduce a simple approach to understanding the relationship between single nucleotide polymorphisms (SNPs), or groups of related SNPs, and the phenotypes they control. The pipeline involves training deep convolutional neural networks (CNNs) to differentiate between images of plants with reference and alternate versions of various SNPs, and then using visualization approaches to highlight what the classification networks key on. We demonstrate the capacity of deep CNNs at performing this classification task, and show the utility of these visualizations on RGB imagery of biomass sorghum captured by the TERRA-REF gantry. We focus on several different genetic markers with known phenotypic expression, and discuss the possibilities of using this approach to uncover genotype x phenotype relationships.