• Control Systems
October 25, 2021

Investigating Irrigation Management Strategies with AquaCrop

An elective usage of irrigation water is required since the world's water demand is increasing. To develop irrigation controllers pursuing this purpose, reinforcement learning implementation could be an interesting step towards smarter automated controllers. This article investigates if Q-learning can be used in order to minimize irrigation amount.

Population growth along with economic growth and changing consumption behaviors results in an inevitable increase in food and water demand. Yet, the world’s resources of clean water are shrinking due to climate change, depletion of groundwater, and pollution. Globally, the largest domains for water demand are agriculture, industry, and domestic usage. Agriculture composes for 70 percent of the world’s water demand where the majority of the water is used for irrigation purposes. Since water usage cannot exceed water availability, higher

effective usage of water in agriculture is needed. Still, the most crucial parameter for plant growth is suffcient soil moisture. Within agriculture, agriculturists must supply crops with enough water while preventing overwatering. The plant experience water stress when root zone water is limited and the wilting point is reached when no water is left to extract. Depending on the intensity and duration of water shortage, crop damages could be irreversible.

Thereby, supplying the crops with more water than necessary reduces the risk of plant stress. The field capacity is the maximum soil moisture content where water will not drain due to gravity. The volume of water between the field capacity and wilting point, is referenced as the Total Available Water, TAW, and can be used in the process of creating irrigation controllers. Irrigation controllers can operate with and without feedback from the field. Due to open-loop control’s low-cost properties, open-loop controls are more used worldwide, but the utilization of closed-loop controls is increasing. An example of a closed-loop controller, could be a controller fitted with a soil moisture sensor while trying to keep the soil moisture at constant level.

Challenges of constructing irrigation controllers, is due to the evapotranspiration. This is the sum of water losses due to soil moisture evaporating into air and plants exchanging gas with its atmosphere in a process called transpiration. Due to the complex nature of the soil water dynamics, finding the optimal soil moisture target compose a challenging model problem. AquaCrop is a crop model simulation tool which enables scientist and engineers to develop irrigation controllers before the real experiment. With this simulation tool, it is possible to develop irrigation strategies without conducting costly field experiments.

The aim was to investigate irrigation management strategies and reduce water demand within agriculture. The goal was to minimize seasonal irrigation while only obtaining a small deviation from the maximum possible final yield value. To obtain this, reinforcement learning implementation was conducted within the software AquaCrop. For this, two different irrigation strategies were investigated – net amount irrigation and irrigation with soil moisture target. The goal with this implementation was to find which irrigation would provide sufficiently large yield, but with as little water as possible. To complement the results from this, two grid searches were performed which were used for comparison.

The reinforcement learning implementation included an agent which took different actions in terms of irrigation strategies and was rewarded a bonus depending on the final yield and water usage. The reinforcement algorithms were able to find certain optimal policies if the percentage of max yield to be maintained is sufficiently low, at around 90% for most of the time. The conclusion of this thesis was that it is possible to deploy irrigation management strategies with reinforcement learning and AquaCrop, given a reward system with lower constraints. The Q-learning implementation was however not able to find the same optimal strategies as the grid search. By comparing the irrigation strategy with net amount irrigation and soil moisture target, it could be seen that in general, the irrigation with soil moisture target could generate a higher performance by finding strategies with less water. This is due to the net amount irrigation not considering the water uptake in each crop stage. Furthermore, it would be of interest to test additional optimization strategies in order to decrease irrigation water while maximizing yield.

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