Feature Extraction and Machine Learning for Classification Date Fruit

Abstract

Dates are important in many parts of the world, particularly in North Africa and the Middle East. As a highly nutritious fruit with strong demand in both local and international markets, the classification and quality control of dates play a crucial role in enhancing their commercial value. This work focuses on improving date fruit classification by applying data augmentation techniques to enrich the original dataset, and then we employed three pre-trained CNN models, ResNet50, EfficientNetB0, and DenseNet201, for feature extraction. The extracted features were then classified using traditional machine learning algorithms: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR), and Random Forest (RF). The bestperformance was achieved using ResNet50 as a feature extractor withlogistic regressionforclassification, reaching anaccuracyof 97.42%.