Classification of Date Fruits into Genetic Varieties Using Image Analysis

This study explores the classification of date fruit varieties in the Kingdom of Saudi Arabia using advanced image processing techniques and genotype analysis. Utilizing a unified model with 30 epochs and 640x640 pixel resolution, we achieved a classification accuracy of $99.382 \%$ with both median and Gaussian filters. Notably, misclassifications were observed between Sugaey and Sokari varieties. The training loss exhibited a sharp initial decrease, stabilizing at a low value, while the validation loss showed fluctuations, particularly around the 20th epoch. Top-1 accuracy increased rapidly, reaching near $100 \%$ by epoch 10, with Top- 5 accuracy consistently higher. The low pass and high pass filters demonstrated similar trends, with Top-1 accuracy stabilizing near $90 \%$ and misclassifications predominantly involving the Sugaey variety. These findings underscore the potential of refined image processing techniques to enhance fruit classification, improve crop management, and support genetic enhancement efforts in the date industry, ultimately contributing to economic growth and product quality in Saudi Arabia. From the analysis the following results were obtained. In the median filter, there is a notable misclassification between Sugaey and Sokari. The validation loss decreases but shows some fluctuations, especially around the 20th epoch