Extracts of tomatoes and potatoes as biopesticides: a review
Bibliographic record
Abstract
Pest control is crucial to protect animals and plants in the agriculture industry. Biopesticides, because of being environmentally friendly and renewable, have attracted more attention in recent years. Nonetheless, due to costs, issues with controlling pests across various agricultural methods, and supplementary obstacles, biopesticides constitute a minor portion of the worldwide market for crop protection. Agricultural products like tomatoes and potatoes stand as examples of Solanaceae, a significant plant family with numerous economically vital species. From 2022 to 2027, the tomato market is anticipated to have a Compound Annual Growth Rate (CAGR) of 5.6%. Likewise, the worldwide market size for potato starch is predicted to attain a value of $4.9 billion with a market growth of 3.5% CAGR by 2027. After harvest, tomato and potato by-products such as leaves, peels, stems, and so on are generated as by-products, but they have not been effectively utilized. Recent research studies show that extract of the byproducts contains components such as glycoalkaloids, flavonoids, additional phenolics, ketones, and so on, which can be used as biopesticides. For proper pest control and utilization of the by-products in agriculture and related industries, this paper provides a review of recent progress on the research of the active components in the extracts of agricultural by-products of tomato and potato, their roles for pest control, extraction methods, challenges, its future development, and more.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".