There are more than 400 types of dates in the world that are similar in size, shape, colour, fruit texture, taste and maturity, making it difficult for people to memorise them. Identification with artificial intelligence can make labelling dates easier. This research proposes the DenseNet-201 transfer learning method with freeze all the pre-trained layers, re-train all the pre-trained layers, and hyperparameter models for date variety identification. The date dataset was collected from the market with a total of 3,300 images of 11 types of dates, including Ajwa, Bam, Golden, Khalas, Khenaizi, Lulu, Mabroum, Medjool, Safawi, Sukari and Tunisian. The purpose of the research is to identify, analyse the test images and compare and recommend the best performance model to identify the type of dates. The experimental results have resulted in the recommendation that the DenseNet-201 method with the hyperparameter model shows the best performance with an accuracy value of 99.39%.