AI-Based Hybrid Deep Learning for Multiscale Red Tilapia Weight-Range Classification Using UAV Imagery

This study proposed an artificial intelligence framework for red tilapia weight-range classification from UAV-based aerial imagery using two hybrid deep learning models, namely Hybrid CNN-XGBoost and Hybrid EfficientNet-B0-XGBoost. Two image sizes were evaluated: 5 × 5 m images, which preserved the spatial context of the cage culture system, and 2 × 2 m images, which focused on areas with high fish aggregation density. For the 5 × 5 m images, Hybrid CNN-XGBoost achieved the highest performance, with a mean accuracy of 0.988 ± 0.008 (98.8%) at 20 tuning units, whereas Hybrid EfficientNet-B0-XGBoost achieved 0.977 ± 0.021 (97.7%) at 40 tuning units. In addition, Hybrid CNN-XGBoost exhibited a substantially shorter average computational workflow time per image (0.038 ± 0.001 s) than Hybrid EfficientNet-B0-XGBoost (65.007 ± 6.141 s). In contrast, under the 2 × 2 m image condition with limited spatial context, Hybrid EfficientNet-B0-XGBoost outperformed Hybrid CNN-XGBoost, achieving the highest mean accuracy of 0.900 ± 0.010 (90.0%) at 30 tuning units, compared with 0.850 ± 0.022 (85.0%) at 50 tuning units. These findings indicate that larger images improve classification accuracy by preserving spatial context, whereas EfficientNet-B0 enhances deep feature extraction capability and improves classification accuracy when spatial context is limited.