Evidence map›Paper›PMID 42122414›Full record

ArticleSensors (Basel, Switzerland)2026

IoT-Based Precision Irrigation System Featuring Multi-Sensor Monitoring and Scheduled Automated Water-Control Gates for Rice Production.

Mir Nurul Hasan Mahmud, Younsuk Dong, Md Mahbubul Alam, Jinat Sharmin

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 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

4 authors.

Mir Nurul Hasan MahmudIrrigation and Water Management Division, Bangladesh Rice Research Institute, Gazipur 1701, Bangladesh.ORCID 0009-0000-1359-9794
Younsuk DongBiosystems and Agricultural Engineering, Michigan State University, East Lansing, MI 48824, USA.ORCID 0000-0003-2400-1916
Md Mahbubul AlamIrrigation and Water Management Division, Bangladesh Rice Research Institute, Gazipur 1701, Bangladesh.ORCID 0000-0002-3549-996X
Jinat SharminIrrigation and Water Management Division, Bangladesh Rice Research Institute, Gazipur 1701, Bangladesh.ORCID 0009-0006-2682-0084

Funding

United States Department of Agriculture USDA FAS MW-CSA-SA
6 · The paper itself

Abstract

Despite its significant water-saving potential, the adoption of alternate wetting and drying (AWD) irrigation remains limited due to infrastructure constraints and intensive manual monitoring requirements. An automated precision irrigation system was developed and tested at the Bangladesh Rice Research Institute research farm in Gazipur, Bangladesh. The system combined ultrasonic water-level sensors, capacitive soil moisture sensors, an Arduino-based microcontroller, a GSM communication module, and solar-powered automatic control gates. Field performance was evaluated following a Randomized Complete Block Design (RCBD) under four irrigation treatments: IRRISAT, IRRI35, IRRI25, and continuous flooding (CF). The first three irrigation treatments were operated using scheduled daily decision windows, in which irrigation actions were automatically triggered based on predefined schedules and sensor threshold values. In IRRISAT, irrigation started when soil moisture dropped slightly below saturation and stopped at a ponding depth of 5 cm, while IRRI35 and IRRI25 were triggered at volumetric soil water contents of 35% and 25%, respectively, with the same upper cutoff of 5 cm ponding depth; CF served as the control. The IRRI35 treatment achieved a high grain yield (7.76 t ha

Indexed as

alternate wetting and drying (AWD)IoT automated irrigationrice cultivationsoil moisture-based controlwater use efficiency

Identifiers

PMID42122414
PMCPMC13165760

What OpenQuestion holds

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LicenceCC BY
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 OpenQuestion graph.