Non Plant Specific Smart Greenhouse with Convective Drying Unit

This paper presents a thorough analysis of the design and execution of a smart greenhouse system that makes use of machine learning and IoT technology. By automating critical procedures, improving monitoring capabilities, and providing predictive analytics for optimal plant growth and disease detection, the system seeks to completely transform conventional greenhouse agriculture. The greenhouse atmosphere is dynamically maintained by the integration of IoT-enabled actuators controlled by a WiFi module (ESP32). Fans, servo motors, mist sprays, lights, and pumps are automated to maintain ideal conditions. Real-time access to environmental factors is facilitated by cloud-based data storage and remote monitoring. Furthermore, plant growth and disease detection can be predicted using machine learning techniques on Raspberry Pis. A CNN model has been customised to identify specific plant diseases. Additionally, energy efficiency and environmental control are improved by the use of a vortex tube in a new drying and cooling system. Accurate soil moisture measurements are possible using a moisture metre based on microclimate conditions, which improves irrigation management. By addressing the issues with traditional greenhouse agriculture, this comprehensive strategy uses cutting-edge technologies to open the door for productive and sustainable crop production.