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Comparison of On-Policy Deep Reinforcement Learning A2C with Off-Policy DQN in Irrigation Optimization: A Case Study at a Site in Portugal

Alibabaei, Khadijeh ; Gaspar, Pedro D. ; Assunção, Eduardo ; Alirezazadeh, Saeid ; Lima, Tânia M. ; Soares, Vasco N. G. J. ; Caldeira, João M. L. P.

Computers (Basel), 2022-07, Vol.11 (7), p.104 [Periódico revisado por pares]

Basel: MDPI AG

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Título:
Comparison of On-Policy Deep Reinforcement Learning A2C with Off-Policy DQN in Irrigation Optimization: A Case Study at a Site in Portugal
Autor: Alibabaei, Khadijeh ; Gaspar, Pedro D. ; Assunção, Eduardo ; Alirezazadeh, Saeid ; Lima, Tânia M. ; Soares, Vasco N. G. J. ; Caldeira, João M. L. P.
Assuntos: Agricultural production ; Agriculture ; Algorithms ; Cereals ; Crop growth ; Decision making ; Decision support systems ; Deep learning ; Farms ; Generalized linear models ; Humidity ; Internet of Things ; Irrigation ; irrigation optimization ; Irrigation scheduling ; Irrigation systems ; Knowledge representation ; Loam soils ; Machine learning ; Mean square errors ; Neural networks ; on-policy deep reinforcement learning ; Optimization ; Production costs ; Productivity ; Return on investment ; Scheduling ; Sensors ; Software ; Support vector machines ; Tomatoes ; Water conservation ; Water consumption ; Water resources ; Water use
É parte de: Computers (Basel), 2022-07, Vol.11 (7), p.104
Descrição: Precision irrigation and optimization of water use have become essential factors in agriculture 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 maximize 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.
Editor: Basel: MDPI AG
Idioma: Inglês

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Comparison of On-Policy Deep Reinforcement Learning A2C with Off-Policy DQN in Irrigation Optimization: A Case Study at a Site in Portugal

Alibabaei, Khadijeh ; Gaspar, Pedro D. ; Assunção, Eduardo ; Alirezazadeh, Saeid ; Lima, Tânia M. ; Soares, Vasco N. G. J. ; Caldeira, João M. L. P.

Basel: MDPI AG

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