Design and Implementation of an IoT-Enabled Aquatic Monitoring System with Hybrid Deep Learning Models

Abstract

Aquatic environments require continuous monitoring to ensure ecological balance, species health, and efficient resource management. Existing monitoring approaches encounter significant challenges due to dynamic underwater conditions such as varying illumination, turbidity, and background complexity, leading to reduced visibility and unreliable analysis. To address these limitations, an embedded edge-AI framework integrating EfficientNet-B0 for spatial feature extraction and LSTM for temporal sequence modeling is proposed. The system utilizes the Aquatic Intelligent Monitoring Dataset (AIMD-2026), comprising 12,000 images and temporal sequences representing diverse aquatic conditions. Environmental sensing parameters including temperature, total dissolved solids, pH, turbidity, and water level are incorporated to enhance contextual awareness and improve decision-making. Advanced preprocessing techniques ensure data consistency and robustness under challenging underwater conditions. The proposed framework demonstrates improved detection accuracy, temporal consistency, and reduced misclassification compared to conventional approaches. Evaluation using accuracy, precision, recall, and mean Average Precision confirms superior performance. The integration of visual, temporal, and environmental data enables reliable monitoring of fish species, supports maintenance of water quality, and contributes to sustainable aquatic ecosystem management and biodiversity preservation. The system is computationally efficient, scalable, and suitable for real-time deployment in embedded environments for aquaculture and environmental monitoring applications.