ALIBABAEI, Khadijeh [et al.] (2022) - Comparison of on-policy deep reinforcement learning A2C with off-policy DQN in irrigation optimization : a case study at a site in Portugal. Computers. DOI 10.3390/computers11070104
10.3390/computers11070104
Title
Comparison of on-policy deep reinforcement learning A2C with off-policy DQN in irrigation optimization : a case study at a site in Portugal
Subject
agriculture deep learning on-policy deep reinforcement learning irrigation optimization
Relation
This work was funded by FCT/MCTES through national funds and, when applicable, co- funded by EU funds under project UIDB/50008/2020
Date
2022-06-27T08:26:00Z 2022-06-27T08:26:00Z 2022
Description
Precision irrigation and optimization of water use have become essential factors in agricul- ture because water is critical for crop growth. The proper management of an irrigation system should enable the farmer to use water efficiently to increase productivity, reduce production costs, and maxi- mize the return on investment. Efficient water application techniques are essential prerequisites for sustainable agricultural development based on the conservation of water resources and preservation of the environment. In a previous work, an off-policy deep reinforcement learning model, Deep Q-Network, was implemented to optimize irrigation. The performance of the model was tested for tomato crop at a site in Portugal. In this paper, an on-policy model, Advantage Actor–Critic, is implemented to compare irrigation scheduling with Deep Q-Network for the same tomato crop. The results show that the on-policy model Advantage Actor–Critic reduced water consumption by 20% compared to Deep Q-Network with a slight change in the net reward. These models can be developed to be applied to other cultures with high production in Portugal, such as fruit, cereals, and wine, which also have large water requirements. info:eu-repo/semantics/publishedVersion
