Year: 2026
Type: article
Abstract: Advances in artificial intelligence, robotics, and computer vision enable intelligent gardening systems that reduce manual monitoring. This study presents a smart gardening framework integrating a mobile robot with convolutional neural network (CNN) flower recognition. The robot captures garden images, while the CNN classifies flower species and provides plant-specific information for future care decisions. Using 50 training images and 50 independent testing images, the model achieved 92.0% training accuracy and 95.0% testing accuracy. The framework connects robotic image acquisition with automated visual recognition and provides a foundation for future irrigation, fertilization, and autonomous garden-management functions.
DOI: 10.33423/25102b43
Authors: Ting Zhang, Oanh Phan, Jiang Lu
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: bronze
OpenAlex ID: W7214523094