The Effectiveness of Botanical Pesticide as Antifungal on Chili (Capsicum annum L) Disease
Bibliographic record
Abstract
Colletotrichum sp. and Phytophthora capsici are a causative diseases in chili, they are causing damage up to 50%.Inorganic pesticides are commonly used to treat the diseases, but there are many impacts to plant and consumers of chili.As an alternative, botanical pesticide such as Colletotrichum sp. and Phytophthora capsici are developed massively, since they are good for consumer health.The aim of this research was to evaluate the effectiveness of various botanical extract of Jatropha curcas, Toona sureni, Pangium edule, Syzygium aromaticum and Cymbopogon citratus L as natural antifungal against the Colletotrichum sp. and Phytophthora capsici.The results showed that the inhibition on colony development of Colletotrichum sp. and Phytophthora capsici tested was significant.Extract of P. edule inhibited of Colletotrichum sp.growth at 6 DAI up to 74.06% and P.capsici up to 24.03%.At the same times observation (6 DAI) C. citratus L extract inhibited up to 44.38% colony growth of Colletotrichum sp. and 86.82% colony of P. capsici.Extract of Syzygium aromaticum showed the perfect inhibition (100%) on mycelial growth for P. capsici and 91.71% for Colletotrichum sp.Whereas, J. Curcas and T. sureni extract showed insignificant effect for all fungal pathogen.We are presumed that it because the bioactive compound of the extract.In this research we found that the main compounds of S. aromaticum is eugenol up to 70.97% that we know as antimicrobial.
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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".