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.
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.
A potential solution involves integrating unlabeled hyperspectral data with labeled samples through semi-supervised learning and resampling techniques, aiming for accurate and efficient monitoring of nitrogen and potassium levels in rubber leaves without the intensive labor required by traditional methods.
