Learning-based multi-agent MPC for irrigation scheduling Article Swipe
Bernard T. Agyeman
,
Mohamed Naouri
,
Willemijn M. Appels
,
Jinfeng Liu
,
Sirish L. Shah
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.conengprac.2024.105908
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.conengprac.2024.105908
Amid concerns about freshwater scarcity, the agricultural sector faces challenges in water conservation and optimizing crop yields, highlighting the limitations of traditional irrigation scheduling methods. To overcome these challenges, this paper introduces a unified, learning-based predictive irrigation scheduler that integrates machine learning and Model Predictive Control (MPC), while also incorporating multi-agent principles. The proposed framework incorporates a three-stage management zone delineation process, utilizing k-means clustering and hydraulic parameters estimates for optimized agro-hydrological modeling. Long Short-Term Memory (LSTM) networks are employed for accurate and computationally efficient root zone soil moisture modeling. The scheduler, formulated as a mixed-integer MPC with zone control, utilizes the identified LSTM networks to maximize root water uptake while minimizing overall water consumption and fixed irrigation costs. Additionally, the learning-based scheduler adopts a multi-agent MPC paradigm, where decentralized hybrid actor–critic agents and the concept of a limiting irrigation management zone are employed to enhance computational efficiency. Evaluating the performance on a 26.4-hectare field in Lethbridge for the 2015 and 2022 growing seasons demonstrates the superiority of the proposed scheduler over the widely-used triggered scheduling approach in terms of Irrigation Water Use Efficiency (IWUE) and total prescribed irrigation. Notably, the proposed approach achieves water savings between 7 to 23%, coupled with IWUE increases ranging from 10 to 35%.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.conengprac.2024.105908
- OA Status
- hybrid
- Cited By
- 15
- References
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4392682412Canonical identifier for this work in OpenAlex
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https://doi.org/10.1016/j.conengprac.2024.105908Digital Object Identifier
- Title
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Learning-based multi-agent MPC for irrigation schedulingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-03-12Full publication date if available
- Authors
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Bernard T. Agyeman, Mohamed Naouri, Willemijn M. Appels, Jinfeng Liu, Sirish L. ShahList of authors in order
- Landing page
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https://doi.org/10.1016/j.conengprac.2024.105908Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.conengprac.2024.105908Direct OA link when available
- Concepts
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Irrigation scheduling, Computer science, Scheduling (production processes), Model predictive control, Scarcity, Irrigation, Agricultural engineering, Mathematical optimization, Control (management), Artificial intelligence, Engineering, Mathematics, Biology, Microeconomics, Economics, EcologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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15Total citation count in OpenAlex
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2025: 9, 2024: 6Per-year citation counts (last 5 years)
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52Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| publication_date | 2024-03-12 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2896715775, https://openalex.org/W3162555379, https://openalex.org/W6858564447, https://openalex.org/W6843993423, https://openalex.org/W4309348867, https://openalex.org/W2077151867, https://openalex.org/W3021256567, https://openalex.org/W2792623193, https://openalex.org/W3195123205, https://openalex.org/W6650218747, https://openalex.org/W3135473476, https://openalex.org/W2764879626, https://openalex.org/W2218314461, https://openalex.org/W2102456122, https://openalex.org/W3023131821, https://openalex.org/W2103147027, https://openalex.org/W3010977907, https://openalex.org/W3098357169, https://openalex.org/W2539773631, https://openalex.org/W1448113274, https://openalex.org/W3089051791, https://openalex.org/W1965759322, https://openalex.org/W1984667267, https://openalex.org/W2256420211, https://openalex.org/W2085655078, https://openalex.org/W1968916607, https://openalex.org/W2918179498, https://openalex.org/W6843588726, https://openalex.org/W2580371084, https://openalex.org/W6681967612, https://openalex.org/W2887769610, https://openalex.org/W4365813991, https://openalex.org/W1985593343, https://openalex.org/W2011321861, https://openalex.org/W1981313577, https://openalex.org/W2558646635, https://openalex.org/W2982316857, https://openalex.org/W2123871098, https://openalex.org/W3043847312, https://openalex.org/W2112061270, https://openalex.org/W4303980931, https://openalex.org/W2335301285, https://openalex.org/W2130070413, https://openalex.org/W2574698072, https://openalex.org/W2963257680, https://openalex.org/W4396513263, https://openalex.org/W2965757358, https://openalex.org/W2888562230, https://openalex.org/W3151474584, https://openalex.org/W4367860562, https://openalex.org/W2528940545, https://openalex.org/W2736601468 |
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