Integrating machine learning paradigms and mixed-integer model predictive control for irrigation scheduling Article Swipe
Bernard T. Agyeman
,
Mohamed Naouri
,
Willemijn M. Appels
,
Jinfeng Liu
,
Sirish L. Shah
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.08715
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.08715
The agricultural sector currently faces significant challenges in water resource conservation and crop yield optimization, primarily due to concerns over freshwater scarcity. Traditional irrigation scheduling methods often prove inadequate in meeting the needs of large-scale irrigation systems. To address this issue, this paper proposes a predictive irrigation scheduler that leverages the three paradigms of machine learning to optimize irrigation schedules. The proposed scheduler employs the k-means clustering approach to divide the field into distinct irrigation management zones based on soil hydraulic parameters and topology information. Furthermore, a long short-term memory network is employed to develop dynamic models for each management zone, enabling accurate predictions of soil moisture dynamics. Formulated as a mixed-integer model predictive control problem, the scheduler aims to maximize water uptake while minimizing overall water consumption and irrigation costs. To tackle the mixed-integer optimization challenge, the proximal policy optimization algorithm is utilized to train a reinforcement learning agent responsible for making daily irrigation decisions. To evaluate the performance of the proposed scheduler, a 26.4-hectare field in Lethbridge, Canada, was chosen as a case study for the 2015 and 2022 growing seasons. The results demonstrate the superiority of the proposed scheduler compared to a traditional irrigation scheduling method in terms of water use efficiency and crop yield improvement for both growing seasons. Notably, the proposed scheduler achieved water savings ranging from 6.4% to 22.8%, along with yield increases ranging from 2.3% to 4.3%.
Related Topics
Concepts
Irrigation scheduling
Scheduling (production processes)
Irrigation
Computer science
Reinforcement learning
Agricultural engineering
Integer programming
Model predictive control
Ranging
Mathematical optimization
Artificial intelligence
Control (management)
Engineering
Mathematics
Algorithm
Telecommunications
Ecology
Biology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2306.08715
- https://arxiv.org/pdf/2306.08715
- OA Status
- green
- Cited By
- 10
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4380993821
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4380993821Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2306.08715Digital Object Identifier
- Title
-
Integrating machine learning paradigms and mixed-integer model predictive control for irrigation schedulingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-14Full publication date if available
- Authors
-
Bernard T. Agyeman, Mohamed Naouri, Willemijn M. Appels, Jinfeng Liu, Sirish L. ShahList of authors in order
- Landing page
-
https://arxiv.org/abs/2306.08715Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2306.08715Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2306.08715Direct OA link when available
- Concepts
-
Irrigation scheduling, Scheduling (production processes), Irrigation, Computer science, Reinforcement learning, Agricultural engineering, Integer programming, Model predictive control, Ranging, Mathematical optimization, Artificial intelligence, Control (management), Engineering, Mathematics, Algorithm, Telecommunications, Ecology, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 9, 2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| publication_year | 2023 |
| referenced_works_count | 0 |
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| abstract_inverted_index.Canada, | 169 |
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| abstract_inverted_index.dynamics. | 107 |
| abstract_inverted_index.hydraulic | 80 |
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| abstract_inverted_index.leverages | 49 |
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| abstract_inverted_index.primarily | 15 |
| abstract_inverted_index.scarcity. | 21 |
| abstract_inverted_index.scheduler | 47, 62, 117, 191, 216 |
| abstract_inverted_index.Formulated | 108 |
| abstract_inverted_index.challenge, | 136 |
| abstract_inverted_index.challenges | 6 |
| abstract_inverted_index.clustering | 66 |
| abstract_inverted_index.decisions. | 155 |
| abstract_inverted_index.efficiency | 204 |
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| abstract_inverted_index.minimizing | 124 |
| abstract_inverted_index.parameters | 81 |
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| abstract_inverted_index.scheduler, | 163 |
| abstract_inverted_index.schedules. | 59 |
| abstract_inverted_index.scheduling | 24, 197 |
| abstract_inverted_index.short-term | 88 |
| abstract_inverted_index.Lethbridge, | 168 |
| abstract_inverted_index.Traditional | 22 |
| abstract_inverted_index.consumption | 127 |
| abstract_inverted_index.demonstrate | 185 |
| abstract_inverted_index.improvement | 208 |
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| abstract_inverted_index.predictions | 103 |
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| abstract_inverted_index.information. | 84 |
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| abstract_inverted_index.reinforcement | 147 |
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| sustainable_development_goals[0].id | https://metadata.un.org/sdg/6 |
| sustainable_development_goals[0].score | 0.6100000143051147 |
| sustainable_development_goals[0].display_name | Clean water and sanitation |
| citation_normalized_percentile |