Enhancing maize performance through precision phenotyping
  • phenomaize@mrizp.rs
  • Address: Slobodana Bajića 1,11185 Belgrade

PROJECT REFERENCES

Project references

Malambo L. et al. Multitemporal field-based plant height estimation using 3D point clouds generated from small unmanned aerial systems high-resolution imagery. Int. J. Appl. Earth Obs. Geoinf. 2018, 64, 31–42.

Gnädinger F., Schmidhalter U. Digital Counts of Maize Plants by Unmanned Aerial Vehicles (UAVs). Remote Sens. 2017, 9, 544.

Han L. et al. Clustering field-based maize phenotyping of plant-height growth and canopy spectral dynamics using a UAV remote-sensing approach. Front. Plant Sci. 2018, 9, 1638.

Han L. et al. Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data. Plant Methods. 2019, 15, 10.

Tayade R. et al. Utilization of Spectral Indices for High-Throughput Phenotyping. Plants 2022, 11, 1712.

Anderson S et al. Prediction of Maize Grain Yield before Maturity Using Improved Temporal Height Estimates of Unmanned Aerial Systems. Plant Phenome J. 2019, 2, 1–15.

Adak A. et al. Temporal Vegetation Indices and Plant Height from Remotely Sensed Imagery Can Predict Grain Yield and Flowering Time Breeding Value in Maize via Machine Learning Regression. Remote Sens. 2021, 13, 2141.

DeSalvio A.J. et al. Phenomic data-facilitated rust and senescence prediction in maize using machine learning algorithms. Sci Rep. 2022, 12, 7571.

Trachsel S. et al. Estimation of physiological genomic estimated breeding values (PGEBV) combining full hyperspectral and marker data across environments for grain yield under combined heat and drought stress in tropical maize (Zea mays L.). PLoS ONE. 2019, 14, 3.

Lane H. et al. Phenomic selection and prediction of maize grain yield from near-infrared reflectance spectroscopy of kernels.  Plant Phenome J. 2020, 3:e20002.

Guo W. et al. UAS-Based Plant Phenotyping for Research and Breeding Applications. Plant Phenomics. 2021, 9840192.

Matias F. et al. FIELDimageR: An R package to analyze orthomosaic images from agricultural field trials. The Plant Phenome J. 2020, 3:e20005.

Sweet D. et al. Opportunities and challenges in phenotyping row crops using drone-based RGB imaging. The Plant Phenome J. 2022, 5, 1– 20.

Mi Weiß T. et al. Unraveling the potential of phenomic selection within and among diverse breeding material of maize (Zea mays L.). G3. 2022, 12, jkab445.

Dodig D. et al. Image-Derived Traits Related to Mid-Season Growth Performance of Maize Under Nitrogen and Water Stress. Front. Plant Sci. 2019, 10, 814.

Dodig D. et al. Dynamics of Maize Vegetative Growth and Drought Adaptability Using Image-Based Phenotyping Under Controlled Conditions. Front. Plant Sci. 2021, 12, 652116.

Danilevicz M. et al. Maize Yield Prediction at an Early Developmental Stage Using Multispectral Images and Genotype Data for Preliminary Hybrid Selection. Remote Sens. 2021, 13, 3976.

Yang B. et al. The Optimal Phenological Phase of Maize for Yield Prediction with High- Frequency UAV Remote Sensing. Remote Sens. 2022, 14, 1559.