Management of Tuta absoluta Meyrick (Lepidoptera: Gelechiidae) Using Biopesticides on Tomato Crop under Greenhouse Conditions
Bibliographic record
Abstract
Tuta absoluta is the major insect invading tomato crop under greenhouse and open field conditions in Lebanon. Farmers mainly depend on chemical control to reduce damage caused by the larva. The hazard use of chemical agents can lead to resistance accumulation. The objective of this study is to investigate alternative agents like biopesticides to control this pest. Two field trials were conducted at the Lebanese Agricultural Research Institute (LARI) for two years under greenhouse conditions. In 2014, the first trial was conducted in two greenhouses: 1-control greenhouse without insect proof net (CG); 2-double door Greenhouse with insect proof net (DDG). In 2015, the second trial was conducted only in control greenhouse.Four treatments and control (not treated plot) were adopted in both trials. The biopesticides used in this study were Neem azal and Bacillus thuringiensis. Results of the first trial showed that using insect proof net reduced the captured adults on the water trap as compared with control greenhouse and thus reducing the damaged caused by the larva of tomato leaf miner on leaves and fruits. The adopted treatments have shown significant differences in the number of mines/leaf, live larva/leaf and percent of damaged fruits in both trials compared to the control. Applying Bacillius thuringiensis and neem azal separately and mixing them together have shown a promising alternative method to chemical 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) | 0.001 | 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".