Work

Multi-temporal UAV multispectral phenotyping and machine learning for modeling cotton defoliation dynamics across diverse accessions

Year: 2026

Type: article

Abstract: Cotton defoliation is critical for efficient mechanical harvesting, yet its temporal progression across diverse germplasm remains difficult to quantify at high throughput. This study developed a multi-temporal unmanned aerial vehicle (UAV)-based machine-learning framework to characterize post-treatment defoliation dynamics across 419 cotton accessions. Multispectral imagery acquired before and after defoliant application was used to derive spectral and texture features, and an Entropy Weight Method (EWM)-based strategy was used for feature ranking and selection. Multiple regression algorithms were evaluated using accession-grouped cross-validation. Spectral indices provided substantially stronger predictive information than texture features, while relative changes from the pre-treatment baseline further improved model performance. Temporal encoding provided a modest but complementary contribution. Among the evaluated algorithms, Random Forest achieved the best overall performance, with an R 2 of 0.73 and an RMSE of 10.69%. Accession-level trajectory analysis subsequently identified four post-treatment defoliation patterns, with variation primarily reflected in the magnitude and rate of defoliation progression. These results demonstrate the potential of multi-temporal UAV phenotyping for scalable characterization of cotton defoliation dynamics and provide a practical basis for germplasm evaluation and mechanized harvest management.

Source: Plant Phenomics

DOI: 10.1016/j.plaphe.2026.100295