Deep Learning Approach for Fruits Classification

Fruit classification is an extension of object detection and accounts for a sizeable portion of fresh products in the food industry. Fruits were formerly classified, which takes time and necessitates continual human presence. Fruit classification is essential for many industrial applications. Supermarkets all across the world must arrange different fruits in order to appropriately organize their price tags and racks. It is challenging, particularly in these days. In order to manually locate the category of the item being purchased in the system, either the customer or the cashier must first determine what it is. Fruits are categorized and used in agriculture to recognize and group fruits based on characteristics including size, shape, color, texture, ripeness, and edibility. As a result, the farmer is able to sort and grade the fruits to ensure that they arrive at the market in the best possible shape. In this essay, we primarily concentrate on categorizing fruits. Three models—MobileNetv2, ResNet50V2, and DenseNet121—were trained for the classification, and we got the best classification accuracy of 98.97%