Nitrogen (N) is one of the most critical nutrients in winegrape production, influencing vine vigor, fruit composition, and wine quality. Because soil N availability varies spatially and temporally, accurate estimation of leaf N concentration is essential for optimizing fertilization at the individual plant level. In this study, in-field hyperspectral images (400-1000 nm) were collected from four grapevine cultivars (Chardonnay, Pinot Noir, Concord, and Syrah) across two growth stages (bloom and veraison) during the 2022 and 2023 growing seasons at both the leaf and canopy levels. An ensemble feature selection framework was developed to identify the most informative spectral bands for N estimation within individual cultivars, effectively reducing redundancy and selecting compact, physiologically meaningful band combinations spanning the visible, red-edge, and near-infrared regions. At the leaf level, models achieved the highest predictive accuracy for Chardonnay (R^2 = 0.82, RMSE = 0.19 %DW) and Pinot Noir (R^2 = 0.69, RMSE = 0.20 %DW). Canopy-level predictions also performed well, with R^2 values of 0.65, 0.72, and 0.70 for Chardonnay, Concord, and Syrah, respectively. White cultivars exhibited balanced spectral contributions across the visible, red-edge, and near-infrared regions, whereas red cultivars relied more heavily on visible bands due to anthocyanin-chlorophyll interactions. Leaf-level N-sensitive bands selected for Chardonnay and Pinot Noir were successfully transferred to the canopy level, improving or maintaining prediction accuracy across cultivars. These results confirm that ensemble feature selection captures spectrally robust, scale-consistent bands transferable across measurement levels and cultivars, demonstrating the potential of integrating in-field hyperspectral imaging with machine learning for vineyard N status monitoring.

翻译：氮(N)是酿酒葡萄生产中最重要的养分之一，直接影响植株活力、果实成分及葡萄酒品质。由于土壤氮素有效性存在时空变异，准确估算叶片氮浓度对实现个体植株水平的施肥优化至关重要。本研究于2022-2023年生长季在叶片和冠层两个尺度上，采集了四个葡萄品种（霞多丽、黑比诺、康科德、西拉）在两种生长阶段（花期和转色期）的田间高光谱图像（400-1000 nm）。开发了集成特征选择框架，用于识别各品种氮素估算中最具信息量的光谱波段，有效降低冗余并选择覆盖可见光、红边及近红外区域的紧凑型生理意义波段组合。在叶片尺度，模型对霞多丽（R²=0.82，RMSE=0.19 %DW）和黑比诺（R²=0.69，RMSE=0.20 %DW）的预测精度最高。冠层尺度预测表现亦佳，霞多丽、康科德和西拉的R²值分别为0.65、0.72和0.70。白葡萄品种在可见光、红边和近红外区域的光谱贡献相对均衡，而红葡萄品种因花青素-叶绿素相互作用更依赖可见光波段。霞多丽和黑比诺叶片尺度筛选的氮敏感波段成功迁移至冠层尺度，提升或维持了各品种的预测精度。研究结果证实，集成特征选择可捕获具有光谱稳健性、尺度一致性的波段，并能跨测量尺度与品种进行迁移，展示了将田间高光谱成像与机器学习相结合用于葡萄园氮素状态监测的潜力。