Edge-Intelligence for Sustainable Pisciculture: Machine Learning Framework for Real-Time Environmental Monitoring and Weather-Driven Grow Forecasting

imit their usefulness in remote or resource-constrained farming sites. To overcome these limitations, this paper proposes an edge-intelligence framework for sustainable aquaculture management that tightly integrates TinyML-driven environmental sensing, lightweight vision-based object detection, and AI-enhanced weather forecasting. The proposed architecture deploys low-power Internet of Things (IoT) nodes at pond and cage locations to perform localized acquisition of critical water-quality metrics. These nodes run TinyML models to perform preliminary data validation, anomaly screening, and comp

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