EcoSense AI: Smart Environmental Intelligence for Air Quality and Sustainable Cultivation

This paper presents Ecosense AI, a hybrid intelligent framework that integrates Internet of Things based environmental monitoring with machine learning driven crop recommendation. The system utilizes MQ135 and DHT11 sensors to monitor indoor air quality parameters such as temperature, humidity, and gas concentration. The collected data is transmitted to a cloud platform for real time processing and visualization. In addition, a supervised machine learning model is employed to recommend suitable crops based on soil type, environmental conditions, and geographical inputs. The Random Forest classifier is used due to its robustness and high prediction accuracy. The hybrid integration of rule based monitoring and predictive analytics enhances environmental sustainability, improves indoor air quality management, and supports precision agriculture. The proposed system offers a scalable, cost effective, and eco friendly solution for intelligent environmental decision making.