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Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
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High-throughput phenotyping in maize and soybean genotypes using vegetation indices and computational intelligence.

Paulo E Teodoro1, Larissa P R Teodoro2, Fabio H R Baio2

  • 1Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul, MS, Brazil. eduteodoro@hotmail.com.

Plant Methods
|October 30, 2024
PubMed
Summary

High-throughput phenotyping (HTP) vegetation indices (VIs) can predict crop traits. Artificial neural networks (ANNs) using VIs like SAVI, GNDVI, NDVI, and NDRE accurately forecast grain yield and other agronomic variables in maize and soybean.

Keywords:
Glycine maxZea maysArtificial neural networkMultispectral sensorPlant breeding

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Area of Science:

  • Agricultural Science
  • Plant Breeding
  • Remote Sensing

Background:

  • Improving crop breeding efficiency requires accurate phenotypic evaluation of complex agronomic traits like grain yield (GY).
  • High-throughput phenotyping (HTP) offers advanced methods for data acquisition, but understanding its relationship with traditional agronomic variables is crucial.
  • Vegetation indices (VIs) derived from HTP data hold potential for indirectly assessing crop performance.

Purpose of the Study:

  • To investigate the association between agronomic variables and VIs obtained via remote sensing in maize and soybean.
  • To identify computational intelligence models for predicting GY using VIs as input.
  • To evaluate the predictive accuracy of multiple regression models (MLR) and artificial neural networks (ANNs).

Main Methods:

  • Comparative field trials were conducted with maize and soybean genotypes over multiple growing seasons.
  • Unmanned aerial vehicle (UAV)-based multispectral imaging was used to capture canopy reflectance at the R1 growth stage.
  • Various VIs were calculated, and their association with agronomic traits (e.g., plant height, GY) was analyzed using correlation networks and path analysis. MLR and ANN models were developed for prediction.

Main Results:

  • VIs demonstrated significant predictive power for agronomic variables in both maize and soybean.
  • Specific VIs, including Soil-adjusted Vegetation Index (SAVI) and Green Normalized Difference Vegetation Index (GNDVI), showed strong direct effects on maize traits.
  • Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) exhibited positive associations with soybean traits.
  • Artificial neural networks (ANNs) outperformed multiple regression models (MLR) in predicting agronomic variables, achieving higher accuracy.

Conclusions:

  • Vegetation indices derived from HTP are effective tools for predicting key agronomic variables in maize and soybean.
  • ANNs provide a more accurate approach than MLR for GY prediction using VIs.
  • Further research into diverse plant traits and spectral variables will enhance understanding of trait-spectral relationships for advanced crop breeding.