Water Quality Monitoring in Agriculture: Applications, Challenges and Future Prospectus with IoT and Machine Learning

Abstract

Water is one of the key elements involved in maintaining the quality of life. It is essential in the field of agriculture for food production and livestock farming. Water quality is continuously declining as a consequence of the growing trend of urbanization and industrialization, which can harm the environment and human health. In recent years, several researchers in the field of agriculture have shown advancements in techniques with the implementation of the Internet of Things (IoT) Artificial Intelligence (AI) machine learning (ML) and smart technologies. These advancements aim at improving water usage and enhancing the quality and quantity of different crops, with the ability to lower analysis costs, time and facilitate the achievement of management results. This paper outlines an overview of recent studies on water control and management using the IoT and ML models in the field of agriculture. Thus, new technologies for analyzing water quality metrics like pH, temperature, color, turbidity, Total Dissolved Solids (TDS), salinity, and nitrogen are described. It also describes few exposed contests used to draw related study suggestions in the future, such as the use of advanced intelligent tools and strategies for water quality assessment and management in the agriculture sector.