Development of an Irrigation System for Predicting Watering Time with ANFIS Method for Chili Plants

Abstract

Abstract Recently, precision farming has become a necessity due to the increasing global demand for staples and water. Thus, farmers will need the availability of sufficient water and fertile soil to meet these needs. Due to the limited availability of both resources, farmers need solutions that change conventional farming systems. Precision farming is the solution to deliver larger and more profitable yields with fewer resources. Currently, several artificial intelligence-based irrigation models have been proposed to use water more efficiently. However, the limited irrigation capabilities of the previous model make it unsuitable for unpredictable climates. The authors conducted research on ANFIS-based intelligent irrigation systems for irrigation system models and the Internet of Things (IoT) to connect sensors to actuators via the cloud. The daily water requirement parameter for plants can be determined using conventional measurements (Gravimetry), this parameter will be the output parameter in the ANFIS modeling. This modeling is compared with reference measurements (conventional) resulting in a fairly accurate accuracy of 87.5%. The proposed system is simple and affordable which makes the technology more precise.