用高光谱成像+机器学习精准测葡萄叶氮含量，可跨植株和尺度复用模型。
Integrating Feature Selection and Machine Learning for Nitrogen Assessment in Grapevine Leaves using In-Field Hyperspectral Imaging
- 通过集成特征选择找出对氮最敏感的光谱波段，减少冗余。
- 叶级预测准确率最高达R²=0.82，冠层级也达到R²=0.72。
- 模型波段可跨品种、跨测量层级迁移，适合智能果园应用。
氮素是葡萄酒葡萄生产中最重要的营养元素之一，影响植株长势、果实成分与葡萄酒品质。由于土壤氮素时空分布不均，精确估算叶片氮浓度对个体植株优化施肥至关重要。本研究在2022与2023年生长季，采集了四个葡萄品种（霞多丽、黑皮诺、康科德、西拉）在开花期和转色期的田间高光谱图像（400–1000 nm），覆盖叶级与冠层级。提出一种集成特征选择框架，识别各品种中最具信息量的光谱波段，有效降低冗余，选出覆盖可见光、红边与近红外区的紧凑组合。叶级预测中，霞多丽（R²=0.82，RMSE=0.19%DW）与黑皮诺（R²=0.69，RMSE=0.20%DW）表现最佳；冠层级预测中，霞多丽、康科德、西拉的R²分别为0.65、0.72、0.70。白皮品种在可见光、红边与近红外区均有均衡响应，而红皮品种更依赖可见光波段，受花青素-叶绿素相互作用影响。为霞多丽和黑皮诺筛选的叶级氮敏感波段成功迁移至冠层级，保持或提升预测性能。结果表明，该方法可提取跨尺度、跨品种稳定可用的光谱特征，证实了将田间高光谱成像与机器学习结合用于葡萄园氮素监测的潜力。
原文摘要 · Abstract (English)
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.
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