Randomized trial demonstrates automated control improves crop yields in smart agriculture, highlighting resource efficiency.
Modern agriculture faces critical challenges regarding resource management, unpredictable climate variability, and crop yield losses. To address these issues and align with the United Nations Sustainable Development Goal 2 (Zero Hunger), this paper presents a functional Proof of Concept (PoC) of an Internet of Things (IoT) driven smart agriculture system integrated with Artificial Intelligence (AI) advisory capabilities. The architecture comprises a sensing and actuation layer based on an ESP32 microcontroller executing MicroPython, a centralized database on Oracle APEX, and an interactive mobile application developed using MIT App Inventor. The system performs continuous monitoring of ambient temperature, humidity, solar radiation, and water storage levels while providing automated local control for irrigation, climate adjustment, and lighting. Additionally, the mobile platform integrates the Gemini Large Language Model (LLM) API to deliver tailored agronomic recommendations. Experimental validation demonstrates that edge-based decision-making reduces resource wastage and mitigates crop vulnerability. Finally, limitations such as initial setup costs and connectivity dependency are discussed alongside future directions for Edge-AI integration.
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Aragón-Hernández et al. (2026) studied this question.
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