Prediction of harvest-related traits in barley using high-throughput phenotyping data and machine learning.

ABSTRACT: Developing crop varieties that maintain productivity under drought is essential for future food security. Here, we investigated the potential of time-resolved high-throughput phenotyping to predict harvest-related traits and identify drought-stressed plants. Six barley lines (Hordeum vulgare) were grown in a greenhouse environment with well-watered and drought treatments, and dynamically phenotyped using RGB, thermal infrared, chlorophyll fluorescence, and hyperspectral imaging sensors. A temporal phenomic classification model accurately distinguished between drought-treated and control plants, achieving high accuracy (classification accuracy ≥0.97) even when relying solely on predictors from the early drought response phase. Canopy temperature depression at the early stage and RGB-

SUBMITTER: Tietze H 

PROVIDER: S-EPMC12560056 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature