Construction and Evaluation of a Cross-Regional and Cross-Year Monitoring Model for Millet Canopy Phenotype Based on UAV Multispectral Remote Sensing

Abstract

Accurate, high-throughput canopy phenotyping using UAV-based multispectral remote sensing is critically important for optimizing the management and breeding of foxtail millet in rainfed regions. This study integrated multi-temporal field measurements of leaf water content, SPAD-derived chlorophyll, and leaf area index (LAI) with UAV imagery (red, green, red-edge, near-infrared bands) across two sites and two consecutive years (2023 and 2024) in Shanxi Province, China. Various modeling approaches, including Random Forest, Gradient Boosting, and regularized regressions (e.g., Ridge, Lasso), were evaluated for cross-regional and cross-year extrapolation. Results showed that single-site modeling achieved coefficients of determination (R²) up to 0.95, with mean relative errors of 10%–15% in independent validations. When models were transferred between sites, R² generally remained between 0.50 and 0.70, although SPAD estimates exhibited larger deviations under high-nitrogen conditions. Even under severe drought in 2024, cross-year predictions still attained R² values near 0.60. Among these methods, tree-based models demonstrated strong capability for capturing nonlinear canopy trait dynamics, whereas regularized regressions offered simplicity and interpretability. Incorporating multi-site and multi-year data further enhanced model robustness, increasing R² above 0.80 and markedly reducing average prediction errors. These findings demonstrate that rigorous radiometric calibration and appropriate vegetation index selection enable reliable UAV-based phenotyping for foxtail millet under diverse environments and time frames. The proposed approach thus provides strong technical support for precision management and cultivar selection in semi-arid foxtail millet production systems.

Plant phenotyping relevance

UAVマルチスペクトル画像から葉水分、クロロフィル、LAIを推定するモデルを開発・比較し、地域間・年次間で検証しており、植物形質取得手法が研究の中心である。

Code and data availability

公開論文であることは確認できましたが、現在の公式API・許可済み取得経路では本文を自動取得できませんでした。

No evidence-backed public reproduction asset is currently recorded.