<![CDATA[Traditional hydroponic systems largely rely on manual observation and regulation of essential environmental variables, such as pH, nutrient concentration, temperature, and humidity. This dependence often causes inefficiency, inconsistent crop quality, and greater labor requirements. To overcome these limitations, this study proposes an IoT-based Smart Hydroponic System that integrates fuzzy logic control with computer vision using the SSD MobileNet architecture. The objective of this research is to design and implement an intelligent automation framework capable of improving hydroponic cultivation through continuous data monitoring, analytical decision-making, and autonomous environmental adjustment. Within this framework, fuzzy logic dynamically stabilizes nutrient and pH levels, while the SSD MobileNet model analyzes plant images to classify growth stages and determine harvest readiness. Experimental testing produced an average classification loss of 0.1283, demonstrating reliable detection accuracy. Compared with conventional methods, the proposed integration enhances adaptability, precision, and computational efficiency for edge-level IoT applications. This system introduces a novel and scalable approach to precision agriculture, enabling more effective automation and decision making in hydroponic farming. Future studies are encouraged to expand their implementation to various plant species and adaptive learning models for broader applicability.]]>
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