Efficacy of Trichoderma asperellum and Pseudomonas aeroginosa Metabolites Against Fusarium Wilt in Bananas
Bibliographic record
Abstract
Banana cultivation is increasingly compromised by pests and diseases, notably the devastating impact of Fusarium oxysporum f.sp.cubense (Foc) wilt disease in Indonesian plantations.This study was designed to characterize and identify biological agents and to evaluate the effectiveness of their metabolites against Foc wilt.A randomized block design was employed, comprising four treatments with six replications each (n=24).The treatments included: distilled water as a control (T0); metabolites derived from Trichoderma asperellum (T1); metabolites from Pseudomonas aeruginosa (T2); and a combination of metabolites from both T. asperellum and P. aeruginosa (T3).Molecular identification confirmed the agents as T. asperellum and P. aeruginosa, with sequence homology of 100% and 98.46%, respectively.The control group's incubation period spanned 281.52 days, during which 100% disease incidence was recorded.Conversely, treatments utilizing the secondary metabolites of T. asperellum, P. aeruginosa, and their combination exhibited no symptomatology up to the 10-month flowering period.The efficacy of the secondary metabolites in inhibiting Foc infection was thus demonstrated up to the critical flowering stage.Results indicate that both the individual and combined applications of T. asperellum and P. aeruginosa metabolites are effective in suppressing Fusarium wilt in bananas.The integration of these biological agents into disease management strategies offers a promising avenue for mitigating the impact of Foc on banana agriculture.
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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".