Revisiting the Role of Sensors for Shaping Plant Research: Applications and Future Perspectives
Bibliographic record
Abstract
Plant health monitoring is essential for understanding the impact of environmental stressors (biotic and abiotic) on crop production, and for tailoring plant developmental and adaptive responses accordingly. Plants are constantly exposed to different stressors like pathogens and soil pollutants (heavy metals and pesticides) which pose a serious threat to their survival and to human health. Plants have the ability to respond to environmental stressors by undergoing rapid transcriptional, translational, and metabolic reprogramming at different cellular compartments in order to balance growth and adaptive responses. However, plants’ exceptional responsiveness to environmental cues is highly complex, which is driven by diverse signaling molecules such as calcium Ca2+, reactive oxygen species (ROS), hormones, small peptides and metabolites. Additionally, other factors like pH also influence these responses. The regulation and occurrence of these plant signaling molecules are often undetectable, necessitating nondestructive, live research approaches to understand their molecular complexity and functional traits during growth and stress conditions. With the advent of sensors, in vivo and in vitro understanding of some of these processes associated with plant physiology, signaling, metabolism, and development has provided a novel platform not only for decoding the biochemical complexity of signaling pathways but also for targeted engineering to improve diverse plant traits. The application of sensors in detecting pathogens and soil pollutants like heavy metal and pesticides plays a key role in protecting plant and human health. In this review, we provide an update on sensors used in plant biology for the detection of diverse signaling molecules and their functional attributes. We also discuss different types of sensors (biosensors and nanosensors) used in agriculture for detecting pesticides, pathogens and pollutants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".