An Effective Dimensionality Reduction Workflow for the Enhancement of Automated Date Fruit Recognition Utilizing Several Machine Learning Classifiers

The classification of different types of date fruit can be challenging due to their varying angles, separation, and exposure to light. To overcome this, image analysis and pattern recognition techniques can be applied. In this study, a proposed workflow involving filter-based feature selection, principal component analysis (PCA), and recursive feature elimination (RFE) was used to select the most salient features for the classification of seven types of date fruit. Machine learning classifiers were then applied to evaluate the performance of the approach, with support vector machine (SVM) outperforming other classifiers. The proposed approach achieved an overall accuracy of 95% using Fisher’s exact test, PCA, RFE, and SVM. Comparison with previous works revealed that the proposed approach was effective in obtaining efficient outcomes. The study demonstrated the potential of using machine learning techniques for date fruit classification, which can ultimately lead to more efficient and accurate grading and sorting processes.