Automated Classification of Dry Bean Varieties Using XGBoost and SVM Models

TL;DR

This study compares XGBoost and SVM machine learning models for accurately classifying seven dry bean varieties, demonstrating high accuracy and potential for improving seed quality control in agriculture.

Contribution

It introduces a robust comparison of XGBoost and SVM for dry bean classification using a large dataset with PCA and nested cross-validation.

Findings

XGBoost achieved 94.00% accuracy

SVM achieved 94.39% accuracy

Models demonstrate high efficacy in seed classification

Abstract

This paper presents a comparative study on the automated classification of seven different varieties of dry beans using machine learning models. Leveraging a dataset of 12,909 dry bean samples, reduced from an initial 13,611 through outlier removal and feature extraction, we applied Principal Component Analysis (PCA) for dimensionality reduction and trained two multiclass classifiers: XGBoost and Support Vector Machine (SVM). The models were evaluated using nested cross-validation to ensure robust performance assessment and hyperparameter tuning. The XGBoost and SVM models achieved overall correct classification rates of 94.00% and 94.39%, respectively. The results underscore the efficacy of these machine learning approaches in agricultural applications, particularly in enhancing the uniformity and efficiency of seed classification. This study contributes to the growing body of work on precision agriculture, demonstrating that automated systems can significantly support seed quality control and crop yield optimization. Future work will explore incorporating more diverse datasets and advanced algorithms to further improve classification accuracy.