Intelligent Time-based Irrigation System using Predictive Algorithms and Internet of Things

Agricultural irrigation mechanisms are increasing rapidly. Since many years, the growth of agriculture has contributed to the rise of civilizations. Various emerging technologies like Internet of Things (IoT) along with many prediction mechanisms improve the entire agricultural system by examining the field in real-time. Many sensors and microcontrollers along with cloud are integrated to produce fruitful IoT systems. It has not only saved the time for farmers but also minimized the usage of conservable resources such as water and electricity. Also, Machine Learning (ML) is used for various automation activities in the farming industry. Usage of IoT in agriculture begins with automating the irrigation methodologies. Scheduling irrigation at right time with right quantity of water allows saving resources and providing the best care for crops. Sensor-based IoT technologies uses sensors to gather soil data, climatic conditions and transmit this information to farm irrigation systems. The proposed system uses a Real Time Clock (RTC) module, which measures the date and time. The system functions everyday at a particular time for irrigating water to the field. Three ML algorithms namely Multiple Linear Regression (MLR), Random Forest (RF) and K-Nearest Neighbor (KNN) comes into play which measures the rainfall condition. The accuracy of all three algorithms are compared and it is proved that Random Forest gives high accuracy of about 75%. The system operates according to the time and predicted rainfall data obtained by the RF algorithm and turns ON the pump automatically. This system paves the way for water conservation and efficient utilization of resources in the agricultural industry.