Toward sustainable post-harvest practices: A critical review of solar and wind-assisted drying of agricultural produce with integrated thermal storage systems
Bibliographic record
Abstract
Postharvest drying is a critical step in reducing agricultural losses and ensuring food quality, especially in off grid and low-resource regions. This review uniquely explores the integration of solar and wind energy with thermal energy storage (TES) to overcome the intermittency challenges of renewable energy in agricultural drying. Drawing on over 100 studies, it evaluates system configurations, drying principles, and energy transfer mechanisms across various crops, with particular attention to heat-sensitive produce like herbs and fruits. Evidence shows that hybrid systems incorporating TES can achieve up to 70% energy savings and reduce drying time by 50–80%, while improving nutrient and aroma retention. The review categorizes and compares solar dryer types direct, indirect, and mixed mode and assesses passive and active wind-assisted drying for their role in enhancing convective transfer. It also analyzes TES materials (sensible and latent heat) and their integration strategies to stabilize temperature and extend drying cycles. Emerging smart dryers with IoT, AI-based controls, and CFD-optimized designs are discussed alongside their socioeconomic implications for low- and middle-income countries (LMIC). The article identifies key research gaps, including the need for harmonized performance metrics, field-scale validation, and locally manufactured modular systems. This interdisciplinary synthesis informs the development of scalable, climate-resilient drying solutions to enhance food security and rural livelihoods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".