Evaluating the antifungal potential of six essential oils against fungi causing wiltand dieback on olive trees
Bibliographic record
Abstract
Essential oils, known for their antimicrobial properties, are being investigated as natural alternatives to synthetic fungicides in agriculture. This study aimed to assess the chemical composition of six commercial essential oils (clove, tea tree, rosemary, thyme, oregano, and garlic) and to evaluate their fungistatic and/or fungicidal activity against six phytopathogenic fungi that cause significant damage to olive trees in Tunisia. For this purpose, the essential oils' qualitative and quantitative chemical compositions were analyzed using gas chromatography-mass spectrometry. The antifungal activity was assessed using the poisoned substrate method at different concentrations (250, 500, 1000, and 4000 ppm). Results showed that the chemical composition analysis revealed that monoterpenoids were the dominant fraction in all oils except clove and garlic, which were primarily composed of eugenol (96.28%) and trisulfide (31.97%), respectively. The antifungal activity results showed that lower concentrations (250, 500, 1000 ppm) of tea tree, rosemary, thyme, and oregano oils had limited inhibitory effects on the tested fungi. However, Biscogniauxia mediterranea was highly sensitive to clove, garlic, and rosemary oils at 4000 ppm. Fusarium oxysporum, Fusarium solani, Verticillium dahliae, and Lasiodiplodia theobromae were mainly inhibited by clove oil at concentrations ranging from 500 to 4000 ppm, while Rhizoctonia bataticola was inhibited by clove and garlic oils at high concentrations. In conclusion, among the tested essential oils, clove oil demonstrated the highest antifungal efficacy, making it a promising natural alternative to synthetic fungicides for controlling olive tree phytopathogenic fungi.
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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) | 0.000 | 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".