International Journal of Academic Information Systems Research (IJAISR) ISSN: 2643-9026 Vol. 4 Issue 1, January – 2020, Pages: 1-5 www.ijeais.org/ijaisr 1 Grapefruit Classification Using Deep Learning Mohammed M. Abu-Saqer, Mohammed O. Al-Shawwa Faculty of Engineering and Information Technology Al-Azhar University Gaza – Palestine Abstract: Fruit has been recognized as a good source of vitamins and minerals, and for their role in preventing vitamin C and vitamin A deficiencies. People who eat fruit as part of an overall healthy diet generally have a reduced risk of chronic diseases. Fruit are important sources of many nutrients, including potassium, fiber, vitamin C and folate (folic acid). One of important types of fruit is Grapefruit . Grapefruit is a tropical citrus fruit known its sweet and somewhat sour taste. It's rich in nutrients, antioxidants and fiber, making it one of the healthiest citrus fruits you can eat. Research shows that it may have some powerful health benefits, including weight loss and a reduced risk of heart disease. In this paper we presented a system that recognize the two types of Grapefruit Pink and white based on deep learning using python on colab editor . This system may help people to automate their factories , restaurants or anything else need to classify these two types for different use . Keywords: Grapefruit ,Deep Learning, Classification, colab , python. INTRODUCTION Grapefruit is a citrus fruit with a flavor that can range from bittersweet to sour. It contains a range of essential vitamins and minerals. People can consume the fruit whole or as a juice or pulp. The grapefruit first appeared in the 18th century, as a result of crossing a pomelo and an orange. People called it "grapefruit" because it grows in clusters, similar to grapes. The nutrients grapefruit contains may help promote healthy skin and protect against various conditions. They may also play a role in weight maintenance. Figure 1: Samples of Grapefruit DEEP LEARNING Deep Learning (DL) or more commonly known as deep structured learning or hierarchical learning is a division of Machine Learning (ML) which is based on a set of algorithms that attempt to model high-level abstractions in data, [1, 2]. Such algorithms develop a layered, hierarchical architecture of learning and representing data. This hierarchical learning architecture is inspired by artificial intelligence emulating the deep, layered learning process of the primary sensorial areas of the neocortex in human brain, which automatically extracts features and abstractions from underlying data [3, 4, 5]. Based on [6, 7], DL algorithms