Evidence map›Paper›PMID 41545563›Full record

ArticleScientific reports2026

Smart irrigation-based internet of things and cloud computing technologies for sustainable farming.

Abdennabi Morchid, Hassan Qjidaa, Rachid El Alami, Salah Mobayen, Paweł Skruch, Badre Bossoufi

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Abdennabi MorchidLIMAS Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah (SMBA) University, 30000, Fes, Morocco. Abdennabi.morchid@usmba.ac.ma.
Hassan QjidaaSystems and Sustainable Environment Laboratory (SED), Faculty of Engineering Sciences (FSI), Private University of Fez (UPF), Fez, Morocco.
Rachid El AlamiLIMAS Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah (SMBA) University, 30000, Fes, Morocco.
Salah MobayenGraduate School of Intelligent Data Science, National Yunlin University of Science and Technology, Douliou, 640301, Yunlin, Taiwan. mobayens@yuntech.edu.tw.
Paweł SkruchDepartment of Automatic Control and Robotics, AGH University of Science and Technology, Kraków, 30-059, Poland.
Badre BossoufiLIMAS Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah (SMBA) University, 30000, Fes, Morocco.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sustainable water management in agriculture is a major challenge, particularly in regions facing water scarcity and the growing impacts of climate change. The lack of efficiency of traditional irrigation methods often leads to water waste, reduced productivity, and increased pressure on natural resources. In this context, it is imperative to develop innovative solutions to optimize water use while maintaining agricultural performance. This paper proposes a smart irrigation system based on the internet of things (IoT) and cloud computing. The system incorporates several sensors to measure key environmental parameters, such as temperature, air humidity, soil moisture, and water level. An embedded ESP32 microcontroller collects and transmits the data to the thingsBoard cloud platform, where it is analyzed in real time to determine precise irrigation needs. The system's algorithm automatically makes the necessary decisions to activate or deactivate the irrigation pump, ensuring optimal and accurate water management. Experimental results demonstrate that the system significantly reduces water waste while optimizing irrigation based on the actual needs of the soil and crops. Real-time measurements and automated decision-making ensure accurate and efficient irrigation that adapts to fluctuations in environmental conditions. Performance analysis shows that the proposed approach significantly improves water resource management compared to traditional methods. The integration of cloud computing and the IoT facilitates remote monitoring and automated decision-making, making the system adaptable to a variety of crops and agricultural lands. The estimated cost of implementing the smart irrigation system is approximately $44.00, confirming its economic feasibility and appeal to small and medium-sized farms seeking to optimize water use. This solution also helps to build farmers' resilience to climate change and water scarcity. The system presented represents a significant advance in the field of smart and sustainable irrigation. By optimizing water use and improving agricultural productivity, the system directly contributes to food security, water resource conservation, and climate resilience. Thus, this study provides a replicable and adaptable model for the development of large-scale smart and sustainable agricultural solutions.

Indexed as

Cloud computingEmbedded systemsEnvironmental sensorsInternet of things (IoT)Irrigation optimizationSmart agricultureSustainabilityWater managementWater resources management

Identifiers

PMID41545563
PMCPMC12881407

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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 OpenQuestion graph.