Hyperspectral estimation of leaf chlorophyll content in wine grape using transfer learning and three-dimensional radiation transfer model

Accurate and timely monitoring of leaf chlorophyll content (LCC) in wine grapes is essential for assessing their photosynthetic status, optimizing management and planting practices, and improving both yield and quality. Traditional physically-based and empirically-based estimation methods often exhibit uncertainties, and they are inadequately adapted to nonlinear relationships. In this study, a UAV hyperspectral deep learning network (VitiChlNet) for estimating LCC of wine grapes was constructed. The estimation and mapping of LCC of wine grapes of different varieties and growth stages were realized by combining the three-dimensional radiation transfer model (3D RTM) and deep transfer learning (TL) techniques. Firstly, 80,000 canopy reflectance data under different canopy structures and observation conditions were simulated using the LESS model, the simulation dataset was then pre-trained using VitiChlNet. Finally, the model was fine-tuned using the TL technique based on the measured UAV hyperspectral and LCC data. The performance of VitiChlNet was compared to empirically-based and physically-based approaches. Specifically, three machine learning (ML) empirical models, XGBoost, LightGBM, and ridge regression, were developed using five different spectral feature construction strategies: full-spectrum, vegetation indices (VIs), competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and uninformative variable elimination (UVE). Meanwhile, a look-up table (LUT) inversion strategy based on the LESS model was constructed. Furthermore, LCC mapping was conducted for various wine grape varieties across different growth stages. The results demonstrated that VitiChlNet outperformed other methods, exhibiting superior LCC estimation performance (R 2 = 0.69), and is less affected by factors such as nitrogen concentration and different interyears. The application of the TL technique enhanced LCC estimation accuracy and effectively addressed the small sample generalization problem. This study may provide a viable strategy for large-scale monitoring of LCC in wine grapes, which is of substantial significance for planting management practices. • A proposed deep learning network VitiChlNet for leaf chlorophyll content estimation in wine grapes. • Pre-training of the network using 3D radiative transfer model simulation data. • VitiChlNet has higher estimation performance compared to traditional empirical models and physical models. • Contributing to estimating leaf chlorophyll content of different wine grape varieties over a large area.