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Classification and Visualization of Genotype x Phenotype Interactions in Biomass Sorghum

arXiv 2108.04090 ↗First posted 9 Aug 2021q-bio.QM cs.LGcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 47 references that are in the map. First posted on arXiv 9 Aug 2021.

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References · 9

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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  3. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
  4. 2016Using Deep Learning for Image-Based Plant Disease Detectioncited by 216
  5. 2016Deep Fruit Detection in Orchardscited by 38
  6. 2017Leaf Counting with Deep Convolutional and Deconvolutional Networkscited by 11
  7. 2021Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detectioncited by 7
  8. 2017Leveraging multiple datasets for deep leaf countingcited by 6
  9. 2021Multi-resolution Outlier Pooling for Sorghum Classificationcited by 3

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