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In the field of agriculture, the water used for irrigation should be given special treatment, as it is responsible for a large proportion of total water consumption. Irrigation scheduling is critical to food production because it guarantees producers a consistent harvest and minimizes the risk of losses due to water shortages. Therefore, the creation of an automatic irrigation method using new technologies is essential. New methods such as deep learning algorithms have attracted a lot of attention in agriculture and are already being used successfully. In this work, a Deep Q-Network was trained for irrigation scheduling. The agent was trained to schedule irrigation for a tomato field in Portugal. Two Long Short Term Memory models were used as the agent environment. One predicts the total water in the soil profile on the next day. The other one was employed to estimate the yield based on the environmental condition during a season and then measure the net return. The agent uses this information to decide the following irrigation amount. An Artificial Neural Network, a Long Short Term Memory, and a Convolutional Neural Network were used to estimating the Q-table during training. Unlike the Long-Short Terms Memory model, the Artificial Neural Network and the Convolutional Neural Network could not estimate the Q-table, and the agent's reward decreased during training. The comparison of the performance of the model was done with fixed base irrigation and threshold based irrigation. The trained model increased productivity by 11% and decreased water consumption by 20–30% compared to the fixed method. • The creation of a system that can help farmers tell them how much and when to irrigate. • After training, the model can be used by farmers and does not need an expert to use the system. • The training of the model is end-to-end, and no manual adjustments are required. • It uses climate data as input which is easy to collect. • The trained model adjusts the irrigation based on climate change. [ABSTRACT FROM AUTHOR]