APPLYING MACHINE LEARNING FOR ENVIRONMENTAL FACTOR PREDICTION ON DESIGNING IOT APPLIED HYDROPONIC SYSTEM

Abstract

In IoT hydroponics, the integration of Internet of Things (IoT) and Machine Learning (ML) has opened up great opportunities to increase the productivity and smart system’s management. With the collected data from sensors in the environment, the machine learning model would analyze and predict the trend of the environmental factors, this combination not only improves the outcome quality but also helps saving the resources. Integrating machine learning into an IoT hydroponics system could not only create a smart, autonomous and adaptable system to changing conditions of environment in real-time but also optimize resources for a cost-effective and productive hydroponic system. In this study, a novel method was presented for predicting environmental factors using Machine Learning algorithm for smart IoT hydroponic systems. By applying this method, an IoT hydroponic system can predict the trends of environmental factors which affects the plants such as temperature, moisture, pH levels…. The experiment results show that the accuracy of the predicted data is reliable, it reached 94.2% for a day and 92.6% for a week. These results could help users take proactive measures to improve the cultivation quality.