AI-Driven Disease Diagnosis in Nile Tilapia Using Machine Learning
Keywords:
Nile tilapia, disease diagnosis, image processing, intelligent system, machine learningAbstract
Nile tilapia (Oreochromis niloticus) is a crucial species in aquaculture, but disease outbreaks pose significant threats to its production, leading to high mortality rates and severe economic losses. Traditional disease diagnosis methods rely on expert assessments, which are costly, time-consuming, and often inaccessible to small-scale farmers. This study proposes an advanced machine learning approach for disease diagnosis in Nile tilapia using DFYOLO, an optimized version of YOLOv5 designed for real-time and high-accuracy detection. A dataset of 1,795 images of healthy and diseased fish was collected and labeled by aquatic veterinary experts. The model was trained and evaluated using standard performance metrics, achieving an outstanding Precision of 99.75%, Recall of 99.31%, and mean Average Precision (mAP50) of 99.38%, while maintaining real-time processing capability at 93.21 FPS. The findings demonstrate that DFYOLO outperforms conventional models, providing a scalable and cost-effective solution for disease monitoring in aquaculture. Future research will explore its applicability to other aquatic species and integration with environmental monitoring systems for enhanced predictive disease management.
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