Efficient Fish Disease Detection Using Image Processing and Machine Learning in Aquaculture

Aquaculture plays a pivotal role in addressing the global demand for high-quality protein, but disease outbreaks remain a significant challenge, causing substantial economic losses and environmental concerns. This study presents an efficient framework for fish disease detection using image processing and machine learning techniques. The proposed system leverages advanced image processing methods to extract critical features from fish images, such as texture, color, and shape, which are indicative of disease symptoms. These features are then analyzed and classified using machine learning algorithms, including Support Vector Machines (SVM), Random Forest, and Gradient Boosting.The system is designed to automate the detection process, providing aquaculture practitioners with a reliable and scalable solution for early disease diagnosis Extensive experiments conducted on real-world aquaculture datasets demonstrate the framework’s high accuracy and robustness in identifying a variety of fish diseases. By reducing the dependency on manual inspections and traditional diagnostic methods, this approach offers a cost-effective and time-efficient alternative for managing fish health.This research underscores the transformative potential of integrating image processing with machine learning to enhance aquaculture sustainability and productivity, paving the way for smarter and more resilient disease management practices.