Date Fruit Datasets

We identified many different kinds of fruits worldwide, each with unique characteristics. These characteristics, such as color, length, diameter, and shape, help determine the type of fruit. Identifying the type of fruit based on its external appearance can be challenging and time-consuming, requiring expertise.

This study focuses on classifying different types of date fruits, including Barhee, Deglet Nour, Sukkary, Rotab Mozafati, Ruthana, Safawi, and Sagai, using three machine learning methods. Using a computer vision system, we obtained a total of 898 images of these date fruit types. By applying image processing techniques, we extracted 34 features from these images, including morphological features, shape, and color.

Initially, we developed models using two machine learning methods: logistic regression (LR) and artificial neural network (ANN). These models achieved performance results of 91.0% and 92.2%, respectively. Subsequently, we created a stacking model by combining these two models, resulting in a performance improvement to 92.8%.

This study demonstrates that machine learning methods can successfully classify different types of date fruits based on their external appearance.

This dataset is sourced from Kaggle.