An Overview of Low-Cost Approaches for the Postharvest Storage of Fruits and Vegetables for Smallholders, Retailers, and Consumers
Bibliographic record
Abstract
Food loss and waste occur throughout the food supply chain and represent food security and environmental, economic, and societal problems. Fresh fruit and vegetables contribute to over 40% of global food loss and waste. A significant portion of fruit and vegetables loss takes place on the farm during postharvest handling in developing countries, which is linked to smallholders’ financial and geographic constraints in purchasing modern postharvest handling technologies. While in developed countries, waste is the main problem identified at the retail and consumption levels because of inadequate logistics management, storage, and consumer behavior. The loss and waste deprive the population of a significant quantity of healthy food. To address this challenge, cost-effective, easy-to-use, and affordable approaches could be supplied to stakeholders. These strategies encompass the utilization of shading, low-cost packaging, porous evaporative cooling, zero-energy cooling chambers, and pot-in-pot coolers, for reductions in loss in developing countries. Meanwhile, in developed countries, biosensors, 1-methylcyclopropene, and imaging processing are employed to assess the quality of fresh fruit and vegetables at both retail and consumer levels. By exploring these methods, the review aims to provide smallholders, retailers, and consumers with efficient methods for improving produce operating techniques, resulting in reduced losses and waste and higher income.
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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 | 0.001 | 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) | 0.005 | 0.002 |
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".