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Improve Soybean Variety Selection Accuracy Using UAV-Based High-Throughput Phenotyping Technology.

Jing Zhou1, Eduardo Beche1, Caio Canella Vieira1,2

  • 1Division of Plant Science and Technology, University of Missouri, Columbia, MO, United States.

Frontiers in Plant Science
|January 28, 2022
PubMed
Summary
This summary is machine-generated.

Selecting superior soybean genotypes for higher yield is challenging. This study shows that Unmanned Aerial Vehicle (UAV)-based high-throughput phenotyping can identify high-yield soybean lines more effectively than traditional breeder selection.

Keywords:
Unmanned Aerial Vehicle (UAV)high-throughput phenotypingmachine learningsoybean breedingvariety selection

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Area of Science:

  • Agricultural Science
  • Plant Breeding
  • Remote Sensing

Background:

  • Conventional crop breeding relies on labor-intensive, often infeasible, selection methods for traits like yield, especially in large populations.
  • Genotype-environment interactions and complex genetic behaviors complicate traditional selection processes in progeny trials.
  • Developing efficient, high-throughput phenotyping methods is crucial for advancing crop breeding programs.

Purpose of the Study:

  • To evaluate the performance of image-based secondary traits for selecting superior soybean breeding lines.
  • To compare the effectiveness of Unmanned Aerial Vehicle (UAV)-based phenotyping with traditional breeder selection for soybean yield.
  • To determine if a data-driven model can improve the accuracy of selecting high-yield soybean genotypes.

Main Methods:

  • A total of 11,473 soybean progeny rows were evaluated over three years (2018-2020).
  • Manual measurement of six agronomic traits alongside data collection using a UAV-based multispectral imaging system.
  • Extraction of image features to develop secondary traits and application of a least absolute shrinkage and selection operator (LASSO) model for genotype selection.

Main Results:

  • Breeder selection significantly improved soybean seed yield compared to non-selected genotypes.
  • The LASSO model, using image-derived traits, identified 71% and 76% of breeder-selected genotypes in Progeny Trials (PT) and Preliminary Yield Trials (PYT).
  • Model-based selections demonstrated a 4-5% higher average yield than breeder selections in PT and PYT, respectively.

Conclusions:

  • Unmanned Aerial Vehicle (UAV)-based high-throughput phenotyping offers a promising approach for efficient selection of high-yield soybean genotypes.
  • Image-based secondary traits and data-driven models can enhance the accuracy and efficiency of crop breeding programs.
  • This technology has the potential to accelerate genetic gain in soybean breeding by improving selection accuracy.