Automated Real-Time Sugarcane Node Detection System Using YOLOv8 and Edge Computing

Sugarcane farming is a vital agricultural sector that faces major challenges due to its heavy reliance on manual labor, especially during node identification and harvesting. Traditional methods are labor-intensive, time-consuming, and prone to human errors, leading to decreased productivity and increased operational costs. This research presents an automated, real-time sugarcane node detection and cutting system leveraging deep learning and computer vision technologies. The proposed system utilizes the YOLOv8 object detection model for accurate node identification and integrates actuator control mechanisms to automate cutting. A dataset comprising over 4000 labeled sugarcane stem images was used to train the detection model, achieving an average precision of ~92% and an F1 score of ~90.5%. The solution is deployed on a low-power edge device (Jetson Nano) using Python, OpenCV, and TensorFlow/Keras, enabling efficient real-time performance with an average processing speed of 20–25 FPS. Servo motors are employed for executing precise cuts based on model predictions. This integration significantly reduces dependency on manual labor, enhances cutting precision, and promotes sustainable farming practices. The system's modularity, low cost, and adaptability suggest a strong potential for commercial adoption and future expansion into broader smart farming applications.