Integrating Plant Height into Hyperspectral Inversion Models for Estimating Chlorophyll and Total Nitrogen in Rice Canopies

Abstract

Rice undergoes rapid growth and exhibits a high demand for nutrients during the tillering and booting stages. SPAD readings, which reflect relative leaf chlorophyll status, and leaf nitrogen content (LNC) are key indicators of plant nutritional status, directly influencing photosynthetic efficiency and biomass accumulation, while plant height (PH) reflects canopy structure and nutrient availability. Establishing quantitative relationships among these traits at key growth stages is essential for stage-specific precision rice management. In this study, Unmanned Aerial Vehicle (UAV) hyperspectral imagery and ground-truth measurements of SPAD, LNC, and PH were collected from rice fields in Qingbaijiang District, Chengdu, China. Twelve vegetation indices (VIs) were calculated, and three machine learning algorithms—partial least squares regression (PLSR), support vector regression (SVR), and random forest regression (RFR)—were employed to develop stage-specific retrieval models. A stage-specific modeling framework integrating PH with hyperspectral data was developed to statistically enhance estimation accuracy at the tillering and booting stages. The optimal models for SPAD readings and LNC achieved R2 values of 0.916 and 0.936, respectively. The results indicate that integrating canopy structural information with hyperspectral features can improve the estimation accuracy of SPAD-related chlorophyll indicators and nitrogen status in rice. Under the controlled field conditions of this study, the proposed framework provides a plot-scale proof-of-concept demonstration for UAV-based stage-specific nitrogen monitoring.

Plant phenotyping relevance

UAVハイパースペクトル画像と機械学習を統合し、イネのクロロフィル指標・窒素状態を推定する段階別フェノタイピング手法を開発・評価しており、方法が研究の中心である。

Code and data availability

The supplied article blocks describe UAV hyperspectral imagery, ground-truth SPAD/LNC/PH measurements, and Python-based PLSR/SVR/RFR modeling, but contain no data availability statement, public dataset deposit, or author code release with a URL. No paper-specific public asset is identified.

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