Spectral indices in cereal phenotyping under field conditions: possibilities and limitations
Tomasz Góral
Instytut Hodowli i Aklimatyzacji Roślin — Państwowy Instytut Badawczy w Radzikowie (Poland)
Krystyna Rybka
k.rybka@ihar.edu.pl{"en_US":"Granbury High School"} (Poland)
Abstract
Progress in high-throughput plant phenotyping and the development of unmanned aerial vehicles (UAVs) have significantly transformed field plant evaluation. Spectral indices have gained particular importance, enabling nondestructive, quantitative, and reproducible assessment of traits related to plant growth, water status, aging processes, and response to biotic and abiotic stresses. This article presents the physical basis of spectral signal recording, characteristics of the most commonly used sensors in field phenotyping, and a review of selected spectral indices used in cereal research. The biological interpretation of the most important indices is discussed, along with their application in assessing biomass, plant vigor, nutritional status, water stress, aging, and yield estimation. The main limitations associated with interpreting spectral data in the field are also discussed, including the influence of environmental conditions, soil background, signal saturation, and predictive model limitations. Particular attention was paid to current developments, including the integration of multi-detector data and the application of machine learning and artificial intelligence methods to analyze multidimensional phenotypic data. Spectral indices, used in conjunction with structural, thermal, and environmental data, are an important tool for modern field phenotyping and breeding decision-making under variable environmental conditions.
Supporting Agencies
Keywords:
UAV, NDVI, spectral indices, cereal breeding, drought stress, genotype–environment interaction, crop monitoring, remote sensing, machine learningReferences
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Authors
Tomasz GóralInstytut Hodowli i Aklimatyzacji Roślin — Państwowy Instytut Badawczy w Radzikowie Poland
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