Application of an IoT and Machine Learning Smart Irrigation System to Minimize Water Usage Within the Agriculture Sector

Globally, farmers are faced with the dilemma of supplying optimal water for crops amidst the ever-increasing extreme weather conditions. Optimal water supply to crops has both cost and crop productivity implications for farmers. New technological advancements have led towards the developing of smart irrigation systems which ensure the efficient consumption of water during irrigation, mainly by applying Internet of Things (IoT). This research considers three crop types namely, beans, chilli and potato, and their respective threshold soil moisture content values. The results show that when beans, chilli and potato were selected, the system issued a command to irrigate for soil moisture values below the threshold soil moisture content, and not irrigate for values above the threshold moisture content, respectively. Moreover, the use of machine learning will enable the system to reduce the need and the cost for extensive sensor network infrastructure, thereby improving on cost efficiencies reported on smart irrigation systems that incorporate IoT technology.