Microneedle Sensors for Ion Monitoring in Plants. One Step Closer to Smart Agriculture
Bibliographic 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".