Analyzing Biotic and Abiotic Stress Responses in Sorghum Using Comprehensive Field Phenomics Data

Authors:

Emmanuel M. Gonzalez¹* ([email protected]), Ariyan Zarei², Brenda Huppenthal², Jeffrey Demieville¹, Travis Simmons¹, Sebastian Calleja¹, Bruno Rozzi¹, Clay Christenson¹, Yuguo Xiao³, Indrajit Kumar³, Maxwell Braud³, Brian P. Dilkes⁴, Eric Lyons^1,5, Duke Pauli^1,5,6, Andrea L. Eveland³

Institutions:

¹School of Plant Sciences, University of Arizona; ²Department of Computer Sciences, University of Arizona; ³Donald Danforth Plant Science Center; ⁴Department of Biochemistry, Purdue University; ⁵Data Science Institute, University of Arizona; ⁶Center for Agroecosystem Research in the Desert (ARID), Tucson

URLs:

Goals

Use the Field Scanner at the University of Arizona to gather field phenomics data from EMS mutagenized sorghum populations and a custom diversity panel, under both well-watered and water-limited conditions.
Develop software and machine learning (ML) models to analyze field phenomics data, quantifying individual plant traits to study plant responses to biotic and abiotic stresses.
Leverage trait data to investigate genotype-phenotype associations and elucidate gene

Abstract

Sorghum [Sorghum bicolor (L.) Moench], the fifth most cultivated cereal crop, is increasingly grown in the U.S. due to its adaptability to marginal lands and diverse uses as a food, feed, and biofuel crop (Ndlovu et al 2022; Yang et al 2022; Hossain et al 2022). Expanding sorghum cultivation requires understanding its natural and induced resistance to biotic and abiotic stress. Recent technological advances have resulted in small, low-cost, and high resolution sensors that can be used to rapidly collect phenotypic trait data at regular time intervals in field or greenhouse settings (Li et al 2020; Sooriyapathirana et al 2021). Today, high spatial and temporal resolution field phenomics data are being collected to extract information on dynamic plant responses to abiotic and biotic stress under real world field conditions.

The University of Arizona houses the world’s largest outdoor plant phenotyping system, the Field Scanner. It uses various sensors to collect plant trait data, including red-green-blue (RGB), photosystem II (PSII) chlorophyll fluorescence, thermal imagery, and 3D point clouds. This raw data is processed using PhytoOracle, a collection of scalable, modular pipelines for phenomic data (Gonzalez et al 2023). The PhytoOracle (PO) pipelines facilitate extraction of phenotypic trait data at multiple levels, from whole plants to individual organs. The ML models segment plant point clouds to gather detailed morphological data. This includes traditional shape descriptors like height, volume, and angle. Additionally, topological data analysis (TDA) is used to study subtle shape nuances. Common TDA methods like persistence diagrams and Euler characteristic curves capture topological signatures for a more nuanced shape study (Amézquita et al 2022; Amézquita et al 2020; Chazal and Michel 2021).

Additionally, ML models are being utilized to identify particular stress factors, including biotic stress. Sorghum, while drought-resistant, is vulnerable to various pathogens, including the destructive soil-borne fungus Macrophomina phaseolina (Tassi) Goid. This fungus causes charcoal rot of sorghum (CRS), disrupting the plant’s water and nutrient transport, leading to symptoms often confused with other conditions like drought stress and frost damage. An automated method distinguishing CRS from other stresses could improve selection accuracy, heritability, and genetic gain, ultimately facilitating the development of more resilient crop cultivars. Various ML models trained to identify and quantify CRS in RGB images are available for use on a web-based application where users can easily analyze their own images: https://charcoal-dryrot-quantification.streamlit.app/.

In studying both abiotic and biotic stress factors, this research seeks to enhance crop productivity by pinpointing variations in stress resilience. Through the application of fine-scale phenotyping, this research contributes to the development of improved, climate-resilient crop varieties.

References

Amézquita, E. J., et al. 2020. “The Shape of Things to Come: Topological Data Analysis and Biology, from Molecules to Organisms,” Developmental Dynamics 249, 816–33.

Amézquita, E. J., et al. 2022. “Measuring Hidden Phenotype: Quantifying the Shape of Barley Seeds Using the Euler Characteristic Transform,” Silico Plants 4, diab033.

Chazal, F., and B. Michel. 2021. “An Introduction to Topological Data Analysis: Fundamental and Practical Aspects for Data Scientists,” Frontiers in Artificial Intelligence 4, 667963.

Gonzalez, E. M., et al. 2023. “PhytoOracle: Scalable, Modular Phenomics Data Processing Pipelines,” Frontiers in Plant Science 14, 1112973.

Hossain, Md. S., et al. 2022. “Sorghum: A Prospective Crop for Climatic Vulnerability, Food and Nutritional Security.” Journal of Agriculture and Food Research 8, 100300.

Li, B., et al. 2020. “Phenomics-Based GWAS Analysis Reveals the Genetic Architecture for Drought Resistance in Cotton,” Plant Biotechnology Journal 18, 2533–44.

Ndlovu, E., et al. 2022. “Morpho-Physiological Effects of Moisture, Heat and Combined Stresses on Sorghum bicolor [Moench (L.)] and its Acclimation Mechanisms,” Plant Stress 2, 100018.

Sooriyapathirana, S. D. S. S., et al. 2021. “Photosynthetic Phenomics of Field- and Greenhouse-Grown Amaranths vs. Sensory and Species Delimits,” Plant Phenomics, 1–13.

Yang, Q., et al. 2022. “Genetic Analysis of Seed Traits in Sorghum bicolor that Affect the Human Gut Microbiome,” Nature Communication 13, 5641.

Funding Information

This material is based upon work supported by the U.S. DOE BER Award Numbers DE-SC0020401 and DE-SC0023305, the U.S. DOE Advanced Research Projects Agency-Energy OPEN Award Number DE-AR0001101, and the National Science Foundation CyVerse Award Number DBI-1743442.