Rubber trees are essential for natural rubber, and require precise nutrient management. Traditional methods for assessing nutrient levels are expensive and destructive, but near-infrared (NIR) hyperspectral techniques offer a promising nondestructive alternative. Challenges arise with high-dimensional data, leading to biased results from small and imbalanced datasets. Current research focuses on overcoming these limitations using machine learning and radiative transfer models.
