LSTM-based model predictive control with discrete inputs for irrigation scheduling Article Swipe

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Bernard T. Agyeman , Soumya Ranjan Sahoo , Jinfeng Liu , Sirish L. Shah ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2112.06352

The development of well-devised irrigation scheduling methods is desirable from the perspectives of plant quality and water conservation. In this article, a model predictive control (MPC) with discrete actuators is developed for irrigation scheduling, where a long short-term memory (LSTM) model of the soil-water-atmosphere system is used to evaluate the objective of ensuring optimal water uptake in crops while minimizing total water consumption and irrigation costs. A heuristic method involving a sigmoid function is used in this framework to enhance the computational efficiency of the scheduler. The scheduling scheme is applied to homogeneous and spatially variable fields and the results indicate that the LSTM-based MPC with discrete actuators is able to prescribe optimal or near-optimal irrigation schedules that are typical of irrigation practice.

Related Topics

Computer Science

Irrigation

Sigmoid Function

Agricultural Engineering

Water Conservation

Mathematics

Engineering

Artificial Intelligence

Ecology

Biology

Combinatorics

Concepts

Irrigation scheduling Computer science Model predictive control Scheduling (production processes) Mathematical optimization Irrigation Homogeneous Actuator Low-flow irrigation systems Sigmoid function Agricultural engineering Water conservation Control (management) Mathematics Engineering Artificial intelligence Ecology Artificial neural network Biology Combinatorics

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2112.06352
PDF: https://arxiv.org/pdf/2112.06352
OA Status: green
Cited By: 2
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2112.06352

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Title: LSTM-based model predictive control with discrete inputs for irrigation scheduling

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Language: en

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Publication year: 2021

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Publication date: 2021-12-12

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Authors: Bernard T. Agyeman, Soumya Ranjan Sahoo, Jinfeng Liu, Sirish L. Shah

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Landing page: https://arxiv.org/abs/2112.06352

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Concepts: Irrigation scheduling, Computer science, Model predictive control, Scheduling (production processes), Mathematical optimization, Irrigation, Homogeneous, Actuator, Low-flow irrigation systems, Sigmoid function, Agricultural engineering, Water conservation, Control (management), Mathematics, Engineering, Artificial intelligence, Ecology, Artificial neural network, Biology, Combinatorics

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Cited by: 2

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