Design and Control of an Intelligent Mobile Gardening Robot Supported by Deep Learning-Based Image Processing

In this study, the design and control of a mobile gardening robot based on image processing and artificial intelligence were implemented. The system, running on Raspberry Pi 4B, can distinguish different plant species and weeds using a YOLOv5-based object detection and classification model; it performs tasks such as cutting weeds, watering useful plants, and moving autonomously while avoiding obstacles. Experiments conducted in a pot environment demonstrated that the robot can successfully perform plant detection, classification, and maintenance functions. With these features, the developed robot offers a low-cost and sustainable solution for small and medium-scale agricultural businesses. The mobile gardening robot designed within the scope of the study was experimentally tested for image processing-based plant recognition, autonomous plant orientation, plant cutting, and watering operations for different scenarios. When the results obtained were examined, it was seen that the developed deep learning-based plant detection model achieved an 82% mAP@50 success rate, while the plant classification model achieved a 92.9% mAP@50 success rate.