IOT AND MACHINE LEARNING BASED SMART SOIL IRRIGATION FARMING SYSTEMS

Abstract

Agriculture plays vital role in development of social and economic condition of the all countries in the world. Water is the source of life and also a precious source for agriculture. Emerging trends in agriculture aims at ensuring more productivity, less damage to the land and helpful for the concept of Sustainable Agriculture (12). Crop productivity is mainly dependent on soil moisture level, less moisture result in loss of yield and plants dying. Excess water causes water wastage and root diseases. To maximize crop production the adequate water quantity at right time is required. In the proposed study IOT and Machine Learning based automated irrigation system developed. The system is divided in to three stages, first is circuit building and sensors installation, the second stage is of reading sensor data and checks the present soil moisture level and in third stage motor ON and OFF decisions is taken by considering the crop type. On server the sensor data is get stored, system gives real time data from the sensors. IOT (Internet of Thing) helps to implement Wireless Sensor Network and Machine Learning can manage optimum water utilization for precious farming by analyzing available data. Finally, the experiments are conducted on the samples collected from the farmland of wheat crops. Additionally, two different scenarios are considered to collect the water requirement samples. Based on the experimental and theoretical analysis of water requirements the proposed irrigation system can reduce the water demand by up to 25% as compared to traditional ways of irrigation. Moreover, in comparison of popular valve automation system the proposed multiple value based system reduces the amount of water wastage up-to 22%. Therefore by utilizing the advance computational techniques (IOT and ML), we can reduce the cost of irrigation system and planning.