Evidence map›Paper›PMID 41680246›Full record

ArticleScientific reports2026

Reliability assessment of agricultural sensors evaluated through algal coverage in hydroponic tomato production systems.

Saksonita Khoeurn, No Hyeon Park, Hye Kyoung Jahng, Jaehyuk Jeon, Seungback Jung, Wan-Sup Cho, Yumin Joung, Eunji Lee, Dong Sub Kim

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.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

9 authors.

Saksonita KhoeurnBigDataLabs Co., Ltd., Cheongju, 28644, South Korea.
No Hyeon ParkDepartment of Big Data, Chungbuk National University, Cheongju, 28644, South Korea.
Hye Kyoung JahngDepartment of Management Information Systems, Cheongju, 28644, South Korea.
Jaehyuk Jeoneney Co., Ltd., Seoul, 08513, South Korea.
Seungback JungWHYBIZ Corp, Seoul, 06978, South Korea.
Wan-Sup ChoBigDataLabs Co., Ltd., Cheongju, 28644, South Korea.
Yumin JoungDepartment of Horticulture, Kongju National University, Yesan, 32439, South Korea.
Eunji LeeDepartment of Horticulture, Kongju National University, Yesan, 32439, South Korea.
Dong Sub KimDepartment of Horticulture, Kongju National University, Yesan, 32439, South Korea. dongsub@kongju.ac.kr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In modern agricultural systems, hydroponics represents a crucial advancement in integrating digital technologies and precision farming practices for sensor-mediated cultivation. These systems employ continuous environmental monitoring to enhance operational efficiency and promote plant growth. However, environmental factors and technical issues can undermine data integrity because of faulty sensor performance. Detecting sensor malfunctions during cultivation is challenging. This study investigated whether algal coverage patterns on sensor surfaces could explain observed variations in sensor-recorded environmental parameters in rockwool-based hydroponic tomato systems. In a controlled greenhouse setting, 117 environmental sensors continuously monitored root-zone temperature, relative humidity, pH, and electrical conductivity (EC). Despite uniform conditions, substantial sensor data variation was observed. Post-cultivation analysis revealed marked differences in algal coverage across sensor surfaces. We hypothesized that algae coverage ratios reflect differential nutrient solution distribution within rockwool substrate, potentially explaining sensor data variation. After 3 months, 39 sensors were categorized according to their algal coverage: 22 sensors exhibited high colonization (≥ 90% coverage) and 17 displayed minimal coverage (< 10%). The sensors with significant algal coverage presented markedly elevated substrate relative humidity (85.6% vs. 41.9%) and EC values (0.77 dS/m vs. 0.33 dS/m) than those with minimal coverage. The tomato productivity metrics did not differ significantly, implying potential biological adaptations to water deficiency or hydrodynamic characteristics of the rockwool. These findings suggest that quantifying algal growth may indirectly reflect environmental parameters, particularly relative humidity and EC, while serving as an inferential validation indicator of sensor data reliability in hydroponic systems. This study addresses the crucial challenges in achieving precise environmental monitoring in advanced agricultural systems, optimizing resource utilization, and improving crop production efficiency.

Indexed as

AgricultureEnvironmental MonitoringHydroponicsSolanum lycopersicumElectric ConductivityHumidityReproducibility of ResultsTemperatureHydroponicRockwoolSensor reliabilitySolanum lycopersicum

Identifiers

PMID41680246
PMCPMC12976035

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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.