Leveraging Ensemble Learning for Dry Beans Classification

For dietary and commercial purposes, dry beans are significant. Dry beans offer many advantages, such as energy and protein. It is crucial to the fight against malnutrition and ensuring food security. The most crucial element in the manufacturing of dry beans is the condition of the seed. As the quality of the seed is so important, seed verification is crucial for dry bean farmers and marketplaces. Choosing high-quality seeds is essential to achieving optimal yield. This research uses a variety of machine-learning techniques to classify different varieties of dry beans. For this research work, a dataset from the UCI machine learning repository is used. The outcomes of model evaluation have been compared, and various classifiers have been trained. In the process, the k-fold cross-validation technique is applied with k = 10 as a parameter. This research aims to differentiate between the seven different types of dry beans by using classification algorithms. An accuracy of 82.01%, 93.46%, 94.67%, and 94.64
^%
has been achieved with the k-nearest neighbor, decision tree, naïve Bayes, and random forest classifiers respectively. Also, the voting ensemble classifier has an accuracy of 95.46%. After the experiments, it was observed that the voting ensemble classifier has the highest accuracy. Future research can examine the performance of new algorithms for better results. Additionally, the dataset can be increased to improve the model's accuracy.