Noninvasive, Presymptomatic Detection of Potato Cyst Nematode Infection in Tomato Using Chlorophyll Fluorescence Analysis
Bibliographic record
Abstract
Potato cyst nematodes (PCNs) are notorious pathogens in all major potato production areas worldwide. Mainly due to the low mobility of this soil pathogen, PCN infestations are mostly observed as patches (“foci”) that only cover a fraction of the acreage. In-field presymptomatic localization of these pathogens is valuable, as it would allow for the localized application of control measures. Although the mapping of foci is technically feasible, it is unpractical, as it would require the analysis of numerous soil samples. We investigated whether chlorophyll fluorescence (Chl-F) could be suitable as a rapid, nondestructive method for early PCN detection. To this end, the impact of four Globodera pallida densities on the Chl-F of tomato was investigated in a phenotyping greenhouse for 26 days. Furthermore, the classical plant performance indicators of biomass and root surface area were compared with Chl-F. Thermal dissipation (NPQ) and an estimate of the photosynthetic rate (ΦPSII) responded at 1 day postinoculation, and ΦPSII was most sensitive to low PCN infection levels. Chl-F parameters responded more readily to PCN infection than biomass and root surface area. The maximum quantum yield of photosystem II (Fv/Fm) and the potential activity of photosystem II (Fv/F0) initially increased at low PCN infection levels, whereas a sharp decrease was observed at higher infestation levels. Hence, our data suggest that low PCN levels promoted plant performance before becoming detrimental at higher levels. Although Chl-F allowed for early and sensitive PCN detection, it remains to be investigated whether these signals can be distinguished from those produced by other belowground stressors in the field. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .
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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 | 0.000 |
| 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) | 0.001 | 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".