Toward Autonomous Farming—A Novel Scheme Based on Learning to Prediction and Optimization for Smart Greenhouse Environment Control

The "Autonomous Farming" presented in this project utilizes Raspberry Pi as a versatile microcontroller, orchestrating a comprehensive array of sensors and actuators in conjunction with machine learning algorithms to create an intelligent farming ecosystem. Diverse sensors, including the DHT11 for temperature and humidity, soil moisture sensors, MQ135 for air quality, and LDR sensor for light levels, continuously gather vital environmental data. This data fuels the decision-making process, allowing the system to autonomously manage various farm parameters by using machine learning.

The system's agility is apparent through its utilization of actuators: a natural ventilator for temperature regulation, a DC pump for soil irrigation, a CPU fan for heat mitigation, and an LDR-driven lighting control system. Machine learning algorithms analyze the data from these sensors, predict environmental changes, and trigger the appropriate actuator responses. This synergy between technology and agriculture not only showcases the capabilities of the Raspberry Pi but also highlights the potential of machine learning in modern farming. The outcome is an efficient and sustainable farming environment that enhances crop yield, optimizes resource utilization, and reduces operational costs, aligning with the evolving landscape of   agriculture.

Keywords: Smart Farming, Humidity, DHT11, Actuators, Sensors, Ventilator, intelligent Farming Ecosystem