Use of Arduino-Based Potentiometric Sensors to Measure Leaf Apoplastic pH Changes Mediated by White Mold Infection in Common Bean ( Phaselous vulgaris L.)
Bibliographic record
Abstract
The common bean, Phaseolus vulgaris L., like many crop species is vulnerable to the destructive necrotrophic fungus Sclerotinia sclerotiorum (Lib.) de Bary (Ss), the causal agent of the white mold disease. To slow Ss spread, farmers rely on costly fungicides that problematically are most effective when applied early, during which the plant lacks visual signs of infection. Internally, an early indicator of Ss infection is the acidification or decrease in plant pH caused by the secretion of oxalic acid released by Ss. The objective of this study was to determine if this early drop in apoplastic pH post-Ss infection could be detected using an Arduino platform-based potentiometric pH sensor with a carbon reference electrode on the leaf surface of a common bean. Interestingly, plant pH did not decrease but was statistically unchanged in the cultivars resistant to Ss (WM-12, WM-1, and G122) or intermediate tolerant (Eldorado, ICA Bunsi, and Beryl), while increasing in the susceptible cultivar (Montrose). This hints at possible Ss resistance mechanisms not present in the susceptible cultivars. Importantly, in seven of the common bean cultivars tested, the direction and magnitude of pH change pre and post-Ss infection measured using the carbon sensor were indistinguishable from the labor-intensive manually extracted leaf apoplastic fluid. Therefore, in the future, these sensors could conceivably be used for high-throughput screening of large germplasm collections to identify novel sources of genetic resistance to Ss that could be introduced into elite common bean cultivars to counter the highly destructive white mold disease.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".