Ensemble feature selection on in-field hyperspectral data identifies compact spectral bands that enable nitrogen prediction in grapevine leaves and canopies with R² up to 0.82 at leaf level and transferable performance across scales and cultivars.
, author Brown, J
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Integrating Feature Selection and Machine Learning for Nitrogen Assessment in Grapevine Leaves using In-Field Hyperspectral Imaging
Ensemble feature selection on in-field hyperspectral data identifies compact spectral bands that enable nitrogen prediction in grapevine leaves and canopies with R² up to 0.82 at leaf level and transferable performance across scales and cultivars.