A Reinforcement Learning Approach for Smart Irrigation Systems

In the last years smart irrigation has become an hot topic at the center of the scientific research attention providing to give solutions able to enhance these systems. The growing shortage of water resources, due to climate change and land use, requires more effective water use strategies for agriculture. Given the high consumption by the agricultural sector, we understand how making the consumption of water for irrigation more intelligent leads to a significant improvement in the management of this resource. In this paper we propose an IoT system composed of specific devices that cooperate in order to realize a smart irrigation management system able to correctly and efficiently use the water resource. The proposed architecture is based on sensor nodes flooded in the terrain and able to collect a series of data needed to operate the water irrigation throughout an intelligence decision maker based on a reinforcement learning algorithm modeled through a Markov Decision Process approach. The proposed reinforcement learning smart irrigation system has been compared with a system based on a simple threshold mechanism in terms of water and energy consumption.