Rose Flower Classification and Segregation Based on Color Using Machine Vision

Flower identification is a technique that is used to distinguish different varieties of Rose flower. It is difficult for common people to identify the species of rose based on their color. Traditional sorting techniques are more time-consuming and prone to human error as the whole process is done by manual labor and visual inspection. The device uses a Convolutional Neural Network (CNN) to accurately determine each rose's color by taking pictures of the flowers through a camera that is placed in a top basket. After that, using a rotation mechanism driven by stepper motors, the categorized flowers are systematically deposited into matching color-coded baskets below. The goal of this project is to improve flower handling procedures by carrying eliminating manual sorting and providing precise and efficient rose segregation. The system enables accurate and efficient segregation, removing the need for manual labor in the flower handling process, by integrating stepper motors and micro servos with a Convolutional Neural Network (CNN).