Evidence map›Paper›PMID 31168083›Full record

ReviewNature nanotechnology2019

Nanobiotechnology approaches for engineering smart plant sensors.

Juan Pablo Giraldo, Honghong Wu, Gregory Michael Newkirk, Sebastian Kruss

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature nanotechnology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 120 papers.

0numbers the graph read from it
0cells of the map it votes in
120citing 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

120 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. In situ NIR-IIb imaging of endogenous HNature communications · 2026
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Review
  12. Article
  13. Scalable Photoactive NOAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  14. Article
  15. Article
  16. Article
  17. Effects of Nanomaterials on Crops.Current issues in molecular biology · 2025
    Review
  18. Article
  19. Review
  20. Review

60 more citing papers are in PubMed but not listed here.

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.

Juan Pablo GiraldoDepartment of Botany and Plant Sciences, University of California, Riverside, CA, USA. juanpablo.giraldo@ucr.edu.ORCID http://orcid.org/0000-0002-8400-8944
Honghong WuDepartment of Botany and Plant Sciences, University of California, Riverside, CA, USA.ORCID http://orcid.org/0000-0001-6629-0280
Gregory Michael NewkirkDepartment of Botany and Plant Sciences, University of California, Riverside, CA, USA.ORCID http://orcid.org/0000-0002-1154-7787
Sebastian KrussInstitute of Physical Chemistry, Georg August University Göttingen, Göttingen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nanobiotechnology has the potential to enable smart plant sensors that communicate with and actuate electronic devices for improving plant productivity, optimize and automate water and agrochemical allocation, and enable high-throughput plant chemical phenotyping. Reducing crop loss due to environmental and pathogen-related stresses, improving resource use efficiency and selecting optimal plant traits are major challenges in plant agriculture industries worldwide. New technologies are required to accurately monitor, in real time and with high spatial and temporal resolution, plant physiological and developmental responses to their microenvironment. Nanomaterials are allowing the translation of plant chemical signals into digital information that can be monitored by standoff electronic devices. Herein, we discuss the design and interfacing of smart nanobiotechnology-based sensors that report plant signalling molecules associated with health status to agricultural and phenotyping devices via optical, wireless or electrical signals. We describe how nanomaterial-mediated delivery of genetically encoded sensors can act as tools for research and development of smart plant sensors. We assess performance parameters of smart nanobiotechnology-based sensors in plants (for example, resolution, sensitivity, accuracy and durability) including in vivo optical nanosensors and wearable nanoelectronic sensors. To conclude, we present an integrated and prospective vision on how nanotechnology could enable smart plant sensors that communicate with and actuate electronic devices for monitoring and optimizing individual plant productivity and resource use.

Indexed as

Crops, AgriculturalPlants, Genetically ModifiedBiosensing TechniquesBiotechnologyCrop ProductionHumansNanotechnology

Identifiers

What OpenQuestion holds

Textmetadata
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.