Hyperparameter Tuning with Optuna to optimize the YOLOv11n Model for Weed Detection

Accurate weed detection is essential for maintaining the cleanliness and aesthetic appeal of residential yards. This study aimed to optimize YOLOv11n, a lightweight object detection model, to achieve high precision in weed identification under real-world conditions. The novelty of this study lies in the application of Optuna, an automatic hyperparameter optimization framework, to enhance model performance while maintaining computational efficiency for resource-limited devices such as drones and IoT systems. The research involved data augmentation techniques including crop (0–20% zoom), hue (±20°), saturation (±30%), brightness (±20%), exposure (±15%), and mosaic augmentation. These augmented images were used to train four YOLO nano variants (v5n, v8n, v11n, v12n), which were evaluated using standard metrics: Precision, Recall, F1-Score, and mean Average Precision (mAP). Among the models tested, YOLOv11n with Custom Optuna configuration delivered the highest performance, achieving a 94.6% F1-score and 97.8% [email protected]. These results demonstrate that the optimized YOLOv11n model can support accurate and efficient real-time weed detection in household environments, particularly on edge devices with limited hardware capabilities. This makes it a viable solution for practical implementation in precision agriculture and smart gardening.