IoT and Deep Learning based Smart Greenhouse Disease Prediction

Abstract

Globally, rapid industrialization and urbanization have resulted in a reduction in agricultural acreage and production. As a result of this, and the growing desire for chemical-free organic veggies among educated urban families, greenhouses are rapidly gaining popularity for their specific benefits, particularly in regions with severe weather. They provide the perfect conditions for longer and more productive growth seasons, as well as lucrative harvests. The current article proposes and shows a full IoT -based Smart Greenhouse system that combines monitoring, alerting, cloud storage, automation, and disease prediction into a single, easily deployed package. It constantly monitors environmental variables like as temperature, humidity, and soil moisture to guarantee a better crop production and quick correction in the event of aberrant circumstances. A built-in automated irrigation management system is also included. Finally, for disease detection using leaf pictures, it uses the most efficient deep learning model. Furthermore, using cloud storage to optimise memory and storage, a city dweller may construct a greenhouse and watch it from his home, allowing him to take corrective action as needed.