Fresh Fruit Detection Using Yolo and OpenCV

Fruits are graded using inspections, past experience, and direct observation. The suggested approach rates the freshness of fruits using machine learning. 2D fruit representations are rated using an analysis method based on shape and colour. It's possible that various fruit photos will have similar colour, size, and form values. Therefore, it is not very successful to distinguish and identify fruit photos using colour or shape property analysis approaches. Therefore, in order to improve the precision and perfection of the fruit freshness recognition, we integrated a convolutional neural network (CNN) with a size, shape, and colour-based method. The suggested method clicks on the fruit image to initiate the process. After that, the image goes through a filtration stage to exclude the fruit sample's size, shape, and colour. The training and testing processes for the fruit images are now being conducted using convolutional neural networks. The size, shape, and colour of fruit are depicted in this suggested study using a convolutional neural network, and the results achieved by combining these three features are quite promising.