Lightweight AI model detects tea pests and diseases with high accuracy
A lightweight YOLOv8n-based model reached 0.980 mAP@0.5 for seven tea pest and disease classes while substantially reducing model size and ...
A lightweight YOLOv8n-based model reached 0.980 mAP@0.5 for seven tea pest and disease classes while substantially reducing model size and ...
A 2026 hospital computer-vision study combined small-object detection, wrist-position checks and memory-enhanced motion analysis to identify hazards and abnormal behavior ...
A computational modeling study of 20 common North American tree species found that AI-generated images improved recognition when real training ...
A YOLO11m-scale detector using directional Tversky similarity in its backbone and classification head improved fine-grained traffic sign detection over a ...
A review of 2019–2025 research finds that transformer models show promise for lung cancer detection and classification, but variable validation ...
A comparative study of sugar beet and maize seedling imagery found that a custom CNN outperformed classical classifiers, while U-Net ...
A causal and invariant-learning system reduced cross-domain dance quality-scoring error by 38.7% in a machine-learning study involving 125 dancers and ...
A lightweight pose-estimation model recognized six fitness actions and counted four types of repetitions with 94.37% accuracy in a 200-video ...
AMA-YOLOv8s reached 93.5% mAP@0.5 in a dataset-based evaluation of photovoltaic defect detection.
A new review in Knowledge and Information Systems synthesizes a decade of anomaly detection research across images, videos, and compressed ...
© 2025 Scienmag - Science Magazine
© 2025 Scienmag - Science Magazine