A Low Cost Smart Irrigation Planning Based on Machine Learning and Internet of Things

Abstract

In the farming process irrigation time and the amount of water supply is a critical factors. A significant amount of freshwater is required for this task but after the utilization of water in the irrigation process it is being polluted. In addition, the excessive use of water during the irrigation process can negatively affect crop production. Therefore, we need to provide a balanced amount of water for effective crop production and conservation of water. In this paper, we proposed low-cost irrigation planning with two key aims first is to reduce the installation and maintenance costs of data collection in innovative irrigation systems and second is to control the valve for water supply automatically. In this context, we first provide a review of recent irrigation systems based on the Internet of Things (IoT) and Machine Learning (ML). Next, we introduce a working plan to collect crop water requirements using a soil moisture sensor. Then, an algorithm is proposed to decide the water supply for water treatment. Finally, the experiments are conducted on the samples collected from the farmland of wheat crops. Based on the experimental analysis the theoretical analysis has been carried out to estimate the reduction in water requirements. The results indicate the effectiveness of the proposed irrigation system by reducing the water demand by up to 25% as compared to traditional ways of irrigation.

Keywords: Smart Irrigation, Irrigation Automation, Machine learning, Water Conservation, Food Security, Water Management Plan