Machine Learning based Drought Prediction System using Cloud and IOT - Poonam Behera, Sancy Sanjay, Beri Prathima, Prachi Singh, Sunil D. M. (International Journal of Modern Agriculture, 2021)
Drought is arguably one among the most important threats of temperature change. It impacts the world resulting in lack of food and water. Therefore, there’s a requirement for technological intervention to observe basic data concerning the weather and soil condition accurately, so as to spot, predict and manage drought conditions. A mix of intelligent sensors ,ESP32 together with cloud technology and machine learning algorithm would build knowledge on wetness and salinity of the soil, temperature and humidity on the surface that are accessible to end users. In this paper, the method proposed is soil moisture sensor and DHT 11 sensor connected to the ESP32-S module which will publish the data such as current temperature, humidity and soil moisture. The ESP32 â€“S module uses a HHTP GET request to update server. The cloud and the Flask micro web framework are used to Receive input from the HTTP GET request and store the data .This data is provided to a machine learning algorithm to determine if there will be a drought. Android apps and web apps are developed to get the results that can be viewed by the client therefore providing a mobile client with information to monitor the drought conditions thereby indicating promptly when it is required to take corrective measures.