Akademik Veri Yönetim Sistemi
International Journal of Agriculture, Environment and Food Sciences, cilt.9, ss.1-13, 2025 (TRDizin)
Modern crop recommendation systems must accurately grasp the complex and nonlinear relationships between soil nutrients to support effective agricultural decisions. In this study, we introduce a framework that combines supervised and unsupervised learning through kernel feature fusion, integrating Radial Basis Function (RBF) Kernel Principal Component Analysis (KPCA) and Kernel Linear Discriminant Analysis (KLDA) into a single seven-dimensional embedding. First, six principal components are extracted using RBF-KPCA to capture global nonlinear variance in the raw data. Similarly, in the raw space, an Nystroem-approximated RBF transformation followed by LDA produces a single discriminant axis (KLDA) for better supervised class separation. These features are fused by concatenation and then input into Support Vector Machine (SVM) classifiers (using polynomial and RBF kernels) and a Random Forest (RF) classifier. In the experiments, a publicly available dataset comparing maize and barley based on six soil features was used. The fused representation significantly outperformed raw data and single-embedding methods, with Polynomial SVM increasing by 18.5%, RBF SVM improving by 10.1%, and RF rising by 4.7% over the raw data. These results show that combining unsupervised variance maximization with supervised discriminant projection creates a richer, more discriminative feature space—especially beneficial for SVMs in crop recommendation tasks. Our kernel fusion approach offers a powerful and flexible strategy for precision agriculture, enabling robust decision support without extensive field trials or repeated laboratory tests