Deep Learning-Based Optimal Condition Monitoring System for Plant Growth in an Indoor Smart Hydroponic Greenhouse

초록

This study introduces a deep learning (DL)-based optimal condition monitoring and control system tailored to indoor smart greenhouses, with a novel focus on maintaining symmetry-defined as a dynamic equilibrium among temperature, humidity, and CO2 levels-critical in plant growth. A hydroponic greenhouse prototype was developed to capture real-time climate data at high temporal resolution. A custom 1D convolutional neural network (1D-CNN) optimized via a genetic algorithm (GA) was employed to predict environmental fluctuations, achieving R2 scores up to 0.99 and a standard error of prediction (SEP) as low as 0.35%. The system then actuated climate control mechanisms to restore and maintain symmetry. Experimental validation revealed that plants grown under the symmetry-aware control system exhibited significantly improved growth metrics. The results underscore the potential of integrating symmetry-aware DL strategies into precision agriculture in achieving sustainable and resilient plant production systems.

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