Researc On Fruit Identification And Ripeness Detection

Information Technology

In this project we introduce a new, high-quality, dataset of images containing fruits. We also present the results of some numerical experiment for training a neural network to detect fruits. We discuss the reason why we chose to use fruits in this project by proposing a few applications that could use such classifier. The aim is to build an accurate, fast and reliable fruit detection system. And in addition to it we are also aiming to develop a system which will detect the quality of fruit in the category of edible or inedible. We will be checking the ripeness of the fruit using the images we will be collecting in the database. We will be detecting the fruit in the first step and the name of the fruit will be displayed. Then the fruits will be categorized on the basis of their outer appearance. If there is some fault on the outer part of the fruit it will be categorized inedible. And if the colour of fruit and the outer appearance is without any fault it will be categorized as edible. It will also have some extra features like where were the fruits packed. How many days did it take to reach the seller. We will be adding the details of the date of packaging and how many days it will be fresh so that the customer can easily understand whether to buy the fruits or not. The customer will also get the information of how many days the fruits will be available and if the stock of a particular fruit is not available then at what date it will be again in the market for the customer to buy at that time. In this we will also be doing the billing of quantities i.e. we will be process a total bill of how many fruits were delivered to a particular vendor or shop which will give us an idea whether the sale is upto the mark or not.

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