Role of Deep Learning Models in Intelligent Classification of Various Date Fruit Bunches

This study presents a performance comparison of different deep machine learning models for the intelligent segregation of date fruit bunches of numerous varieties. The shape, size, and color of the various types of dates with different maturity conditions require experienced farmers to estimate the accrual class. This study has used a transfer learning approach on various machine learning models (VGG-19, MobileNetV2, DenseNet, and NASNet) to classify five different kinds of date bunches (Barhi, Khalas, Menifi, Nabout saif, and Sullaj). Each model is trained on a dataset of 11,944 images of five different date classes. The comparison deduces that DenseNet produces the best accuracy, precision, and recall when compared with the performance of other models. The maximum achieved accuracy of the trained DenseNet model is $100 \%$ for the validation dataset and 98.4% for the test dataset.