A mobile-robot-plus-CNN flower recognition framework reached 92.0% training and 95.0% testing accuracy on 50 training and 50 test images
This study presents a smart gardening framework that integrates a mobile robot for image acquisition with a convolutional neural network (CNN) for flower recognition, where the robot captures garden images, the CNN classifies flower species and provides plant-specific information for future care decisions, achieving 92.0% training accuracy and 95.0% testing accuracy on 50 training images and 50 independent testing images, and providing a foundation for future irrigation, fertilization, and autonomous garden-management functions.