Evidence map›Paper›PMID 39727894›Full record

ReviewBiosensors2024

Sensing Technologies for Outdoor/Indoor Farming.

Luwei Wang, Mengyao Xiao, Xinge Guo, Yanqin Yang, Zixuan Zhang, Chengkuo Lee

Abstract readReview
In one paragraph

Review in Biosensors, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Review
  6. A Generative AI-Assisted Piezo-MEMS Ultrasound Device for Plant Dehydration Monitoring.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    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

6 authors.

Luwei WangDepartment of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore.ORCID 0009-0001-0256-0795
Mengyao XiaoDepartment of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore.
Xinge GuoDepartment of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore.
Yanqin YangDepartment of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore.ORCID 0000-0001-9397-4741
Zixuan ZhangDepartment of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore.
Chengkuo LeeDepartment of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore.ORCID 0000-0002-8886-3649

Funding

Agency for Science, Technology and Research (A*STAR) I2301E0027Reimagine Research Scheme (RRSC) A-0009037-02-00Reimagine Research Scheme (RRSC) A-0009037-03-00Reimagine Research Scheme (RRSC) A-0009454-01-00Research Platform for Biomedical and Health Technology, National University of Singapore Su-zhou Research Institute (RP-BHT, NUSRI) gal-rp-bht-006
6 · The paper itself

Abstract

To face the increasing requirement for grains as the global population continues to grow, improving both crop yield and quality has become essential. Plant health directly impacts crop quality and yield, making the development of plant health-monitoring technologies essential. Variable sensing technologies for outdoor/indoor farming based on different working principles have emerged as important tools for monitoring plants and their microclimates. These technologies can detect factors such as plant water content, volatile organic compounds (VOCs), and hormones released by plants, as well as environmental conditions like humidity, temperature, wind speed, and light intensity. To achieve comprehensive plant health monitoring for multidimensional assessment, multimodal sensors have been developed. Non-invasive monitoring approaches are also gaining attention, leveraging biocompatible and flexible sensors for plant monitoring without interference with its natural growth. Furthermore, wireless data transmission is crucial for real-time monitoring and efficient farm management. Reliable power supplies for these systems are vital to ensure continuous operation. By combining wearable sensors with intelligent data analysis and remote monitoring, modern agriculture can achieve refined management, resource optimization, and sustainable production, offering innovative solutions to global food security and environmental challenges.

Indexed as

AgricultureCrops, AgriculturalBiosensing TechniquesEnvironmental MonitoringVolatile Organic CompoundsVolatile Organic Compoundsflexible sensorsmultimodal sensorsoutdoor/indoor farmingplant health monitoringwearable sensors

Identifiers

PMID39727894
PMCPMC11674220

What OpenQuestion holds

Textmetadata
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.