The "Optimizing Greenhouse Conditions for Strawberry Cultivation" project focuses on simulating an AIoT (Artificial Intelligence of Things) system using Node-RED. The system integrates various technologies including Node-RED, MQTT, MySQL, FastAPI, scikit-learn, and Python to optimize greenhouse conditions for strawberry cultivation. The project utilizes three sensors - temperature, humidity, and light - to predict and optimize the ideal conditions for strawberry growth based on telemetry data.

Strawberry cultivation in greenhouses requires precise environmental control to ensure optimal growth and yield. This project integrates various sensors to monitor crucial parameters such as temperature, humidity, and light intensity. Leveraging historical and real-time data, predictive models are employed to anticipate the ideal conditions for strawberry growth, thus enabling proactive adjustments to the greenhouse environment.

• Sensor Integration: Utilizes temperature, humidity, and light sensors to monitor environmental conditions within the greenhouse.
• Predictive Modeling: Implements machine learning models to predict the optimal levels of temperature, humidity, and light for strawberry cultivation.
• Node-RED Simulation: Simulates the AIoT system using Node-RED, facilitating the visualization and interaction with sensor data and predictive insights.
• Data Storage: Stores sensor data and predictive model outputs in a MySQL database for further analysis and historical reference.
• Real-time Control: Enables real-time adjustments to greenhouse conditions based on predictive insights, enhancing cultivation efficiency and yield.
• Dashboard: Provides a dashboard with graphs displaying real-time data from temperature, humidity, and light sensors for easy monitoring and analysis.
• FastAPI Integration: Integrates FastAPI to provide a RESTful API for accessing sensor data and controlling greenhouse conditions programmatically.
• MQTT Communication: Utilizes MQTT protocol for lightweight communication between sensors, Node-RED, and other components of the system.

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• Review of the Use of Convolutional Neural Networks in Agriculture - Guo, P. J., Li, Z., & Yu, R. X.
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