Machine Learning Applications in the Classification of Dates

his work presents an experimental deployment of deep learning architectures (Custom CNN, GoogleNet, and ResNet-50) to mitigate multi-class classification challenges caused by data imbalances in highly similar qualitative food items. The study maps real-world nutritional variances across seven types of commercial dates, proving that GoogleNet delivers the highest robust validation accuracy of 98.57%, making it an effective baseline tool for precision agriculture and food tracking.