Optimization Model of IoT and Machine Learning for Renewable Energy-Powered Aeroponic Systems

Abstract

This study proposes an optimization model integrating Internet of Things (IoT) and Machine Learning (ML) for renewable energy-powered aeroponic systems as a conceptual framework to enhance sustainable agriculture and address global food security challenges. The model is designed to mitigate land degradation, water scarcity, and the impacts of climate variability on crop productivity. It combines IoT-based real-time monitoring of key environmental variables temperature, humidity, pH, electrical conductivity, and light intensity with Long Short-Term Memory (LSTM) networks for time-series prediction of crop growth and resource requirements. Renewable energy sources, particularly solar photovoltaic systems with battery storage, ensure reliable and environmentally friendly power supply. The proposed approach emphasizes predictive optimization, where IoT data streams inform adaptive LSTM algorithms for precise irrigation and nutrient control. Model performance is evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²). Although the study remains conceptual and simulation-based, validation results demonstrate high predictive accuracy and efficiency. This research establishes a foundational framework for subsequent prototype development, experimental validation, and techno-economic evaluation toward scalable, energy-efficient, and sustainable smart farming systems.