Digital Twins: A novel traceability concept for post-harvest handling
Bibliographic record
Abstract
Digital Twins are a novel approach to systems engineering that can help control complex environments and interface humans with them. This is achieved by digitally mirroring a physical asset to provide historical data , monitoring, and predictions of future states. While there are a few applications of Digital Twins to agriculture , none exist for post-harvest grain handling. However, there have been past attempts at integrating computer assistance in grain quality, called expert systems . These systems were largely abandoned due to their inability to keep operators in the loop and the inadequacy of sensors available during the time of expert systems research. By utilizing Digital Twins and modern post-harvest sensors, operators can be provided with a digital representation of inventory and the quality of grain as it moves throughout a facility. This virtual representation also presents a unique opportunity to enhance market traceability. This review focuses on (1) expert systems, their history, and limitations, (2) the history of Digital Twins and their applicability to grain storage and handling, (3) unit operations and the sensors that are common to grain handling facilities, (4) mathematical and computer models to simulate grain handling operations, and (5) a conceptualization of post-harvest Digital Twins, which identifies research gaps where critical questions should be answered if Digital Twins technology is to be considered a logical contender for traceability of commodities post-harvest.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".