Arithmetic Optimization Algorithm with Machine Learning based Smart Irrigation System in IoT Environment

An integration of new technologies like agro-hydroinformatics and computational intelligence, and information technology (IT) roles a vital play in developing a sustainable precision irrigation method with the effectual management of sensed data assuming plants, weather, and soil. Machine learning (ML) is a fast-developing technology for precision irrigation schemes, because of their capability to simulate human decision-making but also resolve the multi-variable, non-linear, and time-variant problems affecting irrigation management. An essential drive of ML is for providing data from preceding experiences and statistical data to machines towards it is carried out their allocated task of resolving a particular problem. Therefore, this study develops an Arithmetic Optimization Algorithm with Machine Learning based Smart irrigation system (AOAML-SIS) technique in IoT environment. Initially, the AOAML-SIS technique exploits the IoT sensors for data collection process. For determining the need for irrigation, the AOALM-SIS technique utilizes multilayer perceptron (MLP) classification model. Since the MLP parameters play a vital role, the parameter tuning process is carried out by the AOA algorithm. The experimental assessment of the AOAML-SIS technique takes place on agriculture data. The simulation values inferred the effectual irrigation classification efficacy of the AOAML-SIS technique over other models.