Diagnosis of Potassium Content in Rubber Leaves Based on Spatial–Spectral Feature Fusion at the Leaf Scale

Hyperspectral imaging (HSI) technology has attracted extensive attention in the field of nutrient diagnosis for rubber leaves. However, the mainstream method of extracting leaf average spectra ignores the leaf spatial information in hyperspectral imaging and dilutes the response characteristics exhibited by nutrient-sensitive local areas of leaves, thereby limiting the accuracy of modeling. This study proposes a spatial–spectral feature fusion method based on leaf-scale sub-region segmentation. It introduces a clustering algorithm to divide leaf pixel spectra into several subclasses, and segments sub-regions on the leaf surface based on clustering results. By optimizing the modeling contribution weights of leaf sub-regions, it improves the modeling and generalization accuracy of potassium diagnosis for rubber leaves. Experiments have been carried out to verify the proposed method, which is based on spatial–spectral feature fusion to outperform those of average spectral modeling. Specifically, after pixel-level MSC preprocessing, when the spectra of rubber leaf pixel regions were clustered into nine subsets, the diagnostic accuracy of potassium content in rubber leaves reached 0.97, which is better than the 0.87 achieved by average spectral modeling. Additionally, precision, macro-F1, and macro-recall all reached 0.97, which is superior to the results of average spectral modeling. Moreover, the proposed method is also superior to the spatial–spectral feature fusion method that integrates texture features. The visualization results of leaf sub-region weights showed that strengthening the modeling contribution of leaf edge regions is conducive to improving the diagnostic accuracy of potassium in rubber leaves, which is consistent with the response pattern of leaves to potassium.