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Updated: Jul 27, 2025
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A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions
Published on: August 5, 2020
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Phenomic data-driven biological prediction of maize through field-based high-throughput phenotyping integration with
Alper Adak1, Myeongjong Kang2, Steven L Anderson3
1Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, USA.
Journal of Experimental Botany
|June 6, 2023
Summary
Field-based high-throughput phenotyping (HTP) using drones improves prediction of maize traits. Temporal phenomic data revealed gene-environment interactions, aiding the development of resilient crops.
Area of Science:
- Plant Science
- Genetics
- Agricultural Technology
Background:
- High-throughput phenotyping (HTP) generates vast data but few discoveries.
- Field-based HTP (FHTP) with drones monitors plant-environment interactions effectively.
Purpose of the Study:
- To predict plant traits using genomic and phenomic data.
- To discover gene-environment interactions influencing plant resilience.
Main Methods:
- Collected flowering dates and plant height on maize recombinant inbred lines (RILs) using Unoccupied Aerial Vehicles (UAVs).
- Integrated Unoccupied Aerial Vehicle (UAV) phenomic data with single nucleotide polymorphism (SNP) genomic data for prediction.
- Conducted genome-wide association study (GWAS) using temporal reflectance phenotypes.
Main Results:
- Combined phenomic and genomic data significantly improved prediction ability for flowering time and plant height.
- Discovered a heat-related candidate gene (hsp18f) through temporal phenomic data analysis.
- Revealed time-dependent associations between maize genotypes and abiotic stresses.
Conclusions:
- High-dimensional phenomic data can predict complex traits across environments.
- Temporal phenomic data are crucial for understanding genotype-specific responses to abiotic stresses and developing resilient crops.
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