ReviewACS sensors2025
Microneedle Sensors for Ion Monitoring in Plants. One Step Closer to Smart Agriculture.
Review in ACS sensors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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.
Who cites it
5 citing papers in PubMed.
- Plant Wearable Sensors for Data-Driven Precision Agriculture: From Multilevel Sensing to Crop-State Assessment and Adaptive Management.Sensors (Basel, Switzerland) · 2026Review
- Enviromics crosstalk between internal and external plant environments for enhanced adaptation andiMeta · 2026Article
- Pierceable, Water-Resistant, and Transparent Nanofilm Electrodes Comprising Carbon Nanotubes for Long-Term Monitoring of Plant Electrophysiology.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Potentiometric Solid-Contact KMembranes · 2026Article
- Ion-selective electrodes: innovations for precision in vivo plant ion monitoring.Mikrochimica acta · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As global demand for food rises and agricultural systems face unprecedented stress from environmental challenges, understanding the role of ions (i.e., key nutrient components) in crop productivity has never been more critical. Unfortunately, current tools for ion analysis in plants rely on destructive sap collection that fails to capture the dynamic changes in ionic concentrations. On the other hand, noninvasive optical methods lack practicality for field applications due to their reliance on expensive equipment and complex operational procedures. Recent advancements in microneedle (MN) sensing technology have demonstrated significant potential for real-time monitoring of plants' health by enabling the direct detection of various important biomarkers, including but not limited to ions. By offering a minimally invasive approach, MN sensors allow continuous in-planta monitoring with precise penetration into plant tissues, ensuring natural growth remains undisturbed. However, the application of MN sensors, especially for in vivo ion measurement, is still in its very early stage. Herein, we delve into the technological potential and application avenues of plant MN sensors, with a focus on tailoring sensor designs to meet the specific requirements of various plant growth environments and analytical performances for ion detection. This perspective paper also introduces the essential relevance of ion levels in plants, provides a comprehensive assessment of existing ion detection methods, and identifies key challenges associated with achieving effective in planta monitoring. Notably, we highlight the potential of MN sensors as a transformative approach for unveiling plant stress responses, optimizing crop yields, and fulfilling diverse roles that bridge the fields of precision agriculture and plant science research.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.