Harnessing hyperspectral imaging and machine learning for rubber tree nutrient management

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.