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Precision agriculture has emerged as an essential approach for addressing global challenges related to food security, sustainable production, and the efficient management of natural resources. Among its most promising innovations, the smart hydroponic greenhouse distinguishes itself through its ability to precisely control the environmental factors that govern plant growth. This article examines the electronic architecture designed to regulate key parameters within a hydroponic greenhouse, including temperature, humidity, pH, electrical conductivity, and lighting. The system integrates a network of sensors, microcontrollers, and automated actuators that work together to monitor real-time conditions and apply corrective actions whenever deviations occur. Control algorithms interpret sensor data and adjust heating, cooling, ventilation, nutrient dosing, and illumination to maintain an optimal and stable environment for plant development. The proposed architecture emphasizes modularity, allowing components to be replaced or upgraded easily, and scalability, making it suitable for both small-scale and commercial installations. Experimental evaluations conducted on a prototype greenhouse revealed significant improvements in the stability of climatic and nutrient-related variables when compared with traditional cultivation methods. Temperature and humidity variations were substantially reduced, and nutrient solution parameters such as pH and electrical conductivity were maintained within tighter tolerances. These enhancements resulted in more consistent growing conditions, which are essential for improving plant health and maximizing yields. Overall, this study demonstrates that integrating intelligent monitoring and automated control into hydroponic systems can greatly enhance their performance. By ensuring precise and continuous regulation of environmental factors, smart hydroponic greenhouses represent a major step forward in modern agriculture and contribute meaningfully to the development of sustainable and efficient food production systems.
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Rakotonanahary et al. (2026) studied this question.
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