Computational Intelligence and IoT in Transforming Agricultural Environmental Control

Agricultural environmental control plays a vital role in improving crop productivity, resource management, and sustainable farming practices. This project presents Computational Intelligence and IoT in Transforming Agricultural Environmental Control using sensor monitoring, machine learning techniques, and automated irrigation systems. The proposed system uses an Arduino microcontroller integrated with pH, soil moisture, DHT11, atmospheric, and MQ135 gas sensors to monitor environmental and soil conditions in agricultural fields. The system continuously monitors parameters such as soil moisture, pH level, temperature, humidity, atmospheric conditions, and gas concentration for efficient agricultural management. A NodeMCU module is used for IoT-based cloud uploading, enabling continuous monitoring and data analysis. An LCD display is used to show sensor readings and environmental conditions, while a buzzer provides alerts during abnormal situations. Machine learning algorithms developed in Python are used for crop prediction based on soil and environmental conditions. If abnormal conditions such as low soil moisture or improper pH levels are detected, the relay-controlled DC water pump is automatically activated to maintain suitable agricultural conditions. The proposed system improves smart farming automation, supports efficient water management, enhances crop prediction accuracy, and promotes sustainable agriculture using IoT and computational intelligence technologies.