Smart Mushroom Farming: Automated Watering System using RNN-DL Model

Mushroom cultivation demands careful management of environmental factors to achieve optimal growth. This study introduces an intelligent mushroom farming system that combines various sensors, an Arduino- based control mechanism, and a deep learning model to automate the irrigation process. The system continuously monitors key environmental variables such as soil moisture, temperature, humidity, and light intensity using sensors. These readings are processed in real-time by an Arduino controller, which modulates the watering system through Pulse Width Modulation (PWM). To determine the ideal watering schedule, a Recurrent Neural Network (RNN) model analyzes both historical sensor data and current environmental conditions. This predictive approach ensures precise water usage, minimizing waste while enhancing crop yield. Testing the system in a controlled setup revealed marked improvements in water efficiency and mushroom growth when compared to conventional techniques. By integrating advanced technologies, this innovative farming solution presents a scalable and sustainable method for improving mushroom cultivation.