Deep learning for strawberry runner detection integrating ground and aerial imaging

Accurate and efficient detection of strawberry runners is crucial for research applications and for developing runner removal solutions in commercial fruit production. This study presented a deep learning-based approach to automate runner detection under field conditions using images collected from diverse platforms. A comprehensive training dataset was assembled, including multiple strawberry varieties, growth stages, and seasons. Images were captured using three approaches: ground imaging (GI) at 0.5 m above ground level (AGL) in a forward view, and aerial imaging at 5 m AGL (AI5) and 10 m AGL (AI10), both in a nadir view. These platforms provided strawberry plant images of varying resolutions to train YOLO-based deep convolutional neural network (DCNN) models for runner detection and segmentation. Models were trained on platform-specific datasets (GI, AI5, and AI10) as well as on an integrated dataset (GI+AI5+AI10). Validation results revealed that detection and segmentation models trained on the integrated dataset outperformed those trained on platform-specific individual datasets, demonstrating stronger generalization across diverse imaging conditions. The detection model achieved F1-scores of 0.79, 0.81, and 0.82 on the GI, AI5, and AI10 validation datasets, respectively, outperforming the corresponding segmentation models and highlighting the benefit of multi-source training. Notably, for runner detection, aerial imaging at 5 m AGL achieved a strong balance between detection accuracy and imaging efficiency, with an F1-score of 0.81, outperforming both ground-based and higher-altitude aerial imagery. This study improved the automation of strawberry runner detection, provided a publicly accessible dataset for training DCNN models for strawberry plant parts detection, and highlighted mid-altitude aerial imaging as a practical solution for high-throughput runner detection.