Enhanced Hyperspectral Forest Soil Organic Matter Prediction Using a Black-Winged Kite Algorithm-Optimized Convolutional Neural Network and Support Vector Machine

Soil Organic Matter (SOM) is crucial for soil fertility, and effective detection methods are of great significance for the development of agriculture and forestry. This study uses 206 hyperspectral soil samples from the state-owned Yachang and Huangmian Forest Farms in Guangxi, using the SPXY algorithm to partition the dataset in a 4:1 ratio, to provide an effective spectral data preprocessing method and a novel SOM content prediction model for the study area and similar regions. Three denoising methods (no denoising, Savitzky–Golay filter denoising, and discrete wavelet transform denoising) were combined with nine mathematical transformations (original spectral reflectance (R), first-order differential (1DR), second-order differential (2DR), MSC, SNV, logR, (logR)′, 1/R, ((1/R)′) to form 27 combinations. Through Pearson heatmap analysis and modeling accuracy comparison, the SG-1DR preprocessing combination was found to effectively highlight spectral data features. A CNN-SVM model based on the Black Kite Algorithm (BKA) is proposed. This model leverages the powerful parameter tuning capabilities of BKA, uses CNN for feature extraction, and uses SVM for classification and regression, further improving the accuracy of SOM prediction. The model results are RMSE = 3.042, R2 = 0.93, MAE = 4.601, MARE = 0.1, MBE = 0.89, and PRIQ = 1.436.