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Plant breeders and agricultural researchers can increase crop productivity byidentifying desirable features, disease resistance, and nutritional content byanalysing the Dry Bean dataset. This study analyses and compares differentSupport Vector Machine (SVM) classification algorithms, namely linear,polynomial, and radial basis function (RBF), along with other popularclassification algorithms. The analysis is performed on the Dry Bean Dataset,with PCA (Principal Component Analysis) conducted as a preprocessing step fordimensionality reduction. The primary evaluation metric used is accuracy, andthe RBF SVM kernel algorithm achieves the highest Accuracy of 93.34%, Precisionof 92.61%, Recall of 92.35% and F1 Score as 91.40%. Along with adeptvisualization and empirical analysis, this study offers valuable guidance byemphasizing the importance of considering different SVM algorithms for complexand non-linear structured datasets.

Benchmarking the Effectiveness of Classification Algorithms and SVM Kernels for Dry Beans