Advancing UAV-based wheat phenology monitoring: A dual-mode framework integrating time-series reconstruction, noise augmentation, and deep learning for robust BBCH estimation

Abstract

Precise monitoring of wheat phenology (BBCH scale) is essential for agricultural optimization, yet UAV-based single-phase monitoring encounters spectral ambiguities where multiple vegetation indices correspond to identical growth stages. A dual-mode framework integrating time-series reconstruction with hybrid deep learning was developed to resolve this limitation. UAV multispectral and digital imagery (333 plots, 2023–2024) enabled reconstruction of daily-resolved vegetation indices, color/texture features, and BBCH stages using Gaussian, PCHIP, and linear fitting to mitigate environmental noise. Synthetic datasets incorporating Gaussian noise (5–100 % relative intensity) simulated field variability. Feature selection was optimized through Competitive Adaptive Reweighted Sampling (CARS) and Variance Inflation Factor (VIF). Hybrid CNN-GRU and CNN-LSTM architectures surpassed standalone networks by resolving spectral ambiguities in single-phase data and leveraging temporal patterns during time-series analysis. Time-series models attained maximum accuracy under noise-free conditions (CNN-GRU: R 2 = 0.90–0.98, RMSE = 3.61–7.65 BBCH units), with accuracy decreasing proportionally to noise intensity. Conversely, single-phase models demonstrated peak performance at 20 % noise intensity (CNN-GRU: R 2 = 0.56–0.70, RMSE = 15.33–17.22 BBCH units), achieving optimal balance between robustness and practicality for real-time farm monitoring. Extreme noise (100 %) distorted feature distributions (7.25–8.73× expansion), validating controlled augmentation. A novel Rate of Phenological Development (RPDW) —quantified as the slope of BBCH progression—was derived to inform breeding programs, while the noise-optimized single-phase approach enables resource-efficient phenology tracking for family farms. This work bridges methodological innovation (adaptive noise strategies, hybrid architectures) with scalable solutions for precision agriculture, advancing UAV-based phenology monitoring in both academic and applied contexts.

Plant phenotyping relevance

UAV画像・時系列再構成・深層学習を統合し、BBCH生育段階を推定するフェノタイピング手法の開発と性能評価が中心である。

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