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In response to the burgeoning global demand for seafood and the challenges ofmanaging fish farms, we introduce an innovative IoT based environmental controlsystem that integrates sensor technology and advanced machine learning decisionsupport. Deploying a network of wireless sensors within the fish farm, wecontinuously collect real-time data on crucial environmental parameters,including water temperature, pH levels, humidity, and fish behavior. This dataundergoes meticulous preprocessing to ensure its reliability, includingimputation, outlier detection, feature engineering, and synchronization. At theheart of our system are four distinct machine learning algorithms: RandomForests predict and optimize water temperature and pH levels for the fish,fostering their health and growth; Support Vector Machines (SVMs) function asan early warning system, promptly detecting diseases and parasites in fish;Gradient Boosting Machines (GBMs) dynamically fine-tune the feeding schedulebased on real-time environmental conditions, promoting resource efficiency andfish productivity; Neural Networks manage the operation of critical equipmentlike water pumps and heaters to maintain the desired environmental conditionswithin the farm. These machine learning algorithms collaboratively makereal-time decisions to ensure that the fish farm's environmental conditionsalign with predefined specifications, leading to improved fish health andproductivity while simultaneously reducing resource wastage, therebycontributing to increased profitability and sustainability. This researcharticle showcases the power of data-driven decision support in fish farming,promising to meet the growing demand for seafood while emphasizingenvironmental responsibility and economic viability, thus revolutionizing thefuture of fish farming.

IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support