Colour Detection of Agriculture Products for State Analysis

The present market demands recognition of state analysis of an agricultural product automatically rather than conventionally checking the maturity stage and ripeness of an agricultural product which is mundane . In this project we are going to determine the state of an agricultural product using machine learning algorithm with the aid of colour detection. Image processing has been a great help in all kinds of fields which also extended its applicability in agriculture as well . Determining the maturity of an agricultural product at the right time will be very much helpful for the farmers . So by implementing this algorithm the colour as well as the state of the fruit will be determined automatically when we click on the image. Libraries we have used are open CV and Pandas which will help to work with images and the statistical data we are using to convert them into RGB colour models through different functions in the jupyter notebook platform. The two important parts in a project are the prepossessing and the state analysis stages. Firstly, the pre-processing stage determines the colour by calculating the distance to tell how close we are to the actual colour and we will choose the one which has the minimum distance. The second stage is mainly to classify the ripeness and state of an agricultural product. This technique also finds its application in detecting synthetic colours in the edible products . Colour detection is the initial set in any image processing technique. In the future it helps the cashier to determine the quality of the agricultural product effectively and quickly by reducing the effort they put in the traditional method .