Evidence map›Paper›PMID 40567763›Full record

ArticlePeerJ. Computer science2025

IoT-based control and monitoring system for hydroponic plant growth using image processing and mobile applications.

Wizman Rofiansyah, Fayza Rizka Zalianty, Firman Ahmad La Ito, Inung Wijayanto, Harfan Hian Ryanu, Indrarini Dyah Irawati

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ. Computer science, 2025. 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.

Wizman RofiansyahSchool of Electrical Engineering, Telkom University, Bandung, West Java, Indonesia.
Fayza Rizka ZaliantySchool of Electrical Engineering, Telkom University, Bandung, West Java, Indonesia.
Firman Ahmad La ItoSchool of Electrical Engineering, Telkom University, Bandung, West Java, Indonesia.
Inung WijayantoSchool of Electrical Engineering, Telkom University, Bandung, West Java, Indonesia.
Harfan Hian RyanuSchool of Electrical Engineering, Telkom University, Bandung, West Java, Indonesia.
Indrarini Dyah IrawatiSchool of Applied Science, Telkom University, Bandung, West Java, Indonesia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The HydroFarm project presents an innovative IoT-based control and monitoring system for hydroponic plant growth, integrating advanced image processing techniques and mobile applications to enhance urban farming practices. This system addresses critical challenges faced by urban farmers, such as limited space and the need for precise environmental management. By employing a comprehensive approach that combines various sensors (DHT22, DS18B20, pH, TDS) with an ESP32 microcontroller, HydroFarm enables real-time monitoring of essential parameters like temperature, humidity, pH, and nutrient levels. A significant novelty of this project lies in its use of a convolutional neural network (CNN) for plant health assessment through image processing. This technique allows for accurate detection of plant conditions, categorizing leaves as healthy or unhealthy based on visual data captured

Indexed as

ControllingConvolutional neural networkHydroponicsImage processingInternet of ThingsMobile applicationsMonitoringUrban farming

Identifiers

PMID40567763
PMCPMC12190697

What OpenQuestion holds

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LicenceCC BY
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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.