Plant Extract Treatments Induce Resistance to Bacterial Spot by Tomato Plants for a Sustainable System
Bibliographic record
Abstract
The aim of this study is to assess the effect of extracts of Nerium oleander, Eucalyptus chamadulonsis and Citrullus colocynthis against bacterial spot disease of tomato and to investigate the induction of resistance by tomato (Solanum lycopersicum) in order to promote a sustainable management system. The antibacterial activity of aqueous and ethanol plant extracts was tested against Xanthomonas axonopodis pv. vesicatoria, isolate PHYXV3, in vitro and in vivo. The highest antibacterial activity in vitro was obtained with C. colocynthis, N. oleander and E. chamadulonsis, respectively. In vivo, ethanol extracts of N. oleander and E. chamadulonsis were more effective than aqueous extracts in reducing pathogen populations on tomato leaves. Under greenhouse conditions, application of the plant extracts at 15% (v/v) to tomato plants significantly reduced disease severity and increased the shoot weight of ‘Super Marmande’ tomato. In most cases, plant extracts significantly increased total phenol and salicylic acid content of tomato plants compared to either healthy or infected ones. In addition, C. colocynthis and E. chamadulonsis extracts significantly increased peroxidase activity while only E. chamadulonsis increased polyphenol oxidase after infection with the causal agent. The results indicated that the plant extracts showed promising antibacterial activity and could be considered an effective tool in integrated management programs for a sustainable system of tomato bacterial spot control.
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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) | 0.002 | 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".