Comparing Deep Learning Approaches for Understanding Genotype × Phenotype Interactions in Biomass Sorghum
Year: 2022
Type: article
Abstract: We explore the use of deep convolutional neural networks (CNNs) trained on overhead imagery of biomass sorghum to ascertain the relationship between single nucleotide polymorphisms (SNPs), or groups of related SNPs, and the phenotypes they control. We consider both CNNs trained explicitly on the classification task of predicting whether an image shows a plant with a reference or alternate version of various SNPs as well as CNNs trained to create data-driven features based on learning features so that images from the same plot are more similar than images from different plots, and then using the features this network learns for genetic marker classification. We characterize how efficient both approaches are at predicting the presence or absence of a genetic markers, and visualize what parts of the images are most important for those predictions. We find that the data-driven approaches give somewhat higher prediction performance, but have visualizations that are harder to interpret; and we give suggestions of potential future machine learning research and discuss the possibilities of using this approach to uncover unknown genotype × phenotype relationships.
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $1,150
Awards: G2277256672, A REFERENCE PHENOTYPING SYSTEM FOR ENERGY SORGHUM, G4202028437, CONTROL NUMBER 1858-3555
DONALD DANFORTH PLANT SCIENCE CENTER
PROJECT TITLE: AUGMENTED REALITY GUI FOR BIOENERGY CROP PHENOTYPING AND PRECISION AGRICULTURE
THIS PROJECT WILL CREATE A SCALABLE ARCHITECTURE FOR COLLABORATIVE PHENOMICS (SACP). SACP WILL PROVIDE A PLATFORM-AGNOSTIC, OPEN SOURCE, ORGANIZATIONAL STRUCTURE FOR CREATION, ANALYSIS AND SHARING OF PHENOMICS DATASETS, ANALYTICAL PIPELINES AND VISUALIZATION SOFTWARE. SACP WILL INCLUDE A FULL STACK WEB SOLUTION FOR SHARING PUBLIC DATASETS AND OPEN SOURCE CODE SOLVING COMMON “PAIN POINTS” IN PHENOMICS. THIS PROJECT WILL PRODUCE A NOVEL VIRTUAL REALITY (VR) AND AUGMENTED REALITY (AR) GRAPHICAL USER INTERFACE (GUI) TO TRANSFORM HOW RESEARCHERS INTERACT WITH COMPLEX DATA. FINALLY, WE WILL CREATE PUBLICLY AVAILABLE “BEST IN CLASS” BENCHMARK DATASETS FOR ANALYSIS WITH MACHINE LEARNING (ML) AND DEEP LEARNING (DL) APPROACHES.
OpenAlex ID: W4283798674