Potential of UAV-derived RGB spectral indices for the early selection of cotton genotypes based on fiber quality traits

Abstract

Abstract This study evaluated the potential of UAV-derived RGB spectral indices to predict fiber quality traits in cotton genotypes. Nineteen genotypes were assessed under a randomized complete block design, and RGB imagery acquired at full flowering was used to calculate GLI, NGRDI, SCId, and SI. Significant genetic variability and moderate-to-high heritability were observed for both fiber traits and spectral indices. GLI was positively associated with the Spinning Consistency Index, whereas NGRDI was associated with fiber length uniformity. Regression models showed moderate predictive ability (R² LOOCV between 32.4–35.4%; and accuracy between 0.57–0.60). GLI and NGRDI demonstrated potential as complementary tools for large-scale phenotyping and preliminary genotype selection, although they do not replace conventional fiber quality analyses. Further studies across additional developmental stages are needed to improve prediction accuracy.

Plant phenotyping relevance

UAV由来RGB画像からスペクトル指標を算出し、ワタの繊維品質形質を予測する手法を評価しており、大規模フェノタイピングへの応用と予測性能の検証が中心である。

Code and data availability

The paper reports UAV RGB imagery, spectral indices, and fiber quality data, but no public dataset, images, or author code repository is deposited. The data statement says raw data are available only from the corresponding author upon request; FIELDimageR and GENES are generic third-party tools, not paper-specific code

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