Evidence map›Paper›PMID 39364235›Full record

ArticleHeliyon2024

Towards a modelling, optimization and predictive control framework for smart irrigation.

Erion Bwambale, Felix K Abagale, Geophrey K Anornu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Erion BwambaleDepartment of Agricultural and Biosystems Engineering, Makerere University, P. O. Box 7062, Kampala, Uganda.
Felix K AbagaleWest African Center for Water, Irrigation and Sustainable Agriculture (WACWISA), University for Development Studies, P. O. Box TL 1882, Tamale, Ghana.
Geophrey K AnornuCivil Engineering Department, Regional Water and Environmental Sanitation Center Kumasi (RWESCK), Kwame Nkrumah University of Sciences and Technology, Kumasi, Ghana.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Smart irrigation scheduling is a promising approach for improving the efficiency and sustainability of agricultural water use, especially in arid and semi-arid lands. In this paper, we present a design and simulation of a model predictive controller for smart irrigation scheduling. The proposed controller is based on a mathematical model of the irrigation system, which is used to predict the future states of the system and determine the optimal irrigation schedule for a given crop and field. This study further evaluates the impact of the predictive control framework on crop yield, water use efficiency (WUE), and water savings in tomato production. Three control strategies, including manual control, open-loop control, and model predictive control (MPC), were compared using a Completely Randomized Design (CRD) methodology. Results indicate that MPC outperforms the other strategies, yielding the highest average crop yield of 20 t/ha and demonstrating superior WUE at 10.4 kg/m

Indexed as

Data-driven modellingDynamics and controlModel predictive controlSmart irrigationSystem identificationWater use efficiency

Identifiers

PMID39364235
PMCPMC11447320

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.