Exploratory Data Analysis and Kernel Feature Fusion for Enhanced SVM and Random Forest–Based Crop Recommendation
Abstract
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.
Keywords
Precision Agriculture, Crop Recommendation, Soil Nutrient Embedding, Kernel Feature Fusion, Artificial Intelligence
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