Design and Development of Smart Automated System for Sugarcane Node Detection and Elimination using Yolo-V8 Techniques

This research presents an automated system for sugarcane node removal, integrating computer vision and machine learning techniques. The proposed system utilizes the YOLOv8 object detection model to accurately identify and locate sugarcane nodes within images captured by a webcam. An Arduino Nano board, connected to a relay module, controls the precise removal of identified nodes. By automating this critical task, the system enhances efficiency, reduces labour costs, and improves consistency compared to manual methods. This innovative approach has the potential to significantly improve sugarcane farming practices, leading to increased yields and enhanced productivity.