Leveraging Blockchain Technology to Improve Traceability in Organic Food Supply Chain Management Using Deep Learning Technique
Bibliographic record
Abstract
Ensuring traceability in the organic food supply chain (OFSC) remains a critical challenge due to the high susceptibility of organic labels to fraud, inefficient documentation, and fragmented stakeholder systems. In this paper, the authors introduce a hybrid architecture that combines the blockchain technology solution with a deep learning framework that combines GRU and Transformer to improve end-to-end traceability, detect fraud, and build trust in the OFSC ecosystem. The layer of a blockchain, developed based on Hyperledger Fabric, provides the ability to record the transactions of a supply chain in an immutable way with the help of smart contracts and data anchoring. Meanwhile, the deep learning model operates on both temporal (e.g. timestamps, GPS) and spatial (e.g. shipping routes, environmental conditions) information to validate the trace paths and realize anomalies in the real time. The experimental analysis of the supply chain across various nodes shows that the proposed GRU + Transformer model gave an accuracy of 97.34%, precision of 95.81, and an F1-score of 92.55%- it was more than the classic models of LSTM (91.64) and CNN (91.21). The blockchain infrastructure ensured a steady throughput of more than 230 TPS and reduced detection latency to less than 2 seconds in giving the important events such as dispatch and retail scan. The solution provides a privacy-sensitive, scalable, and auditable mechanism of tracing organic products. It makes sure of adhering to certification standards yet it has the ability to give the stakeholders clear and smart monitoring tools. The combination of AI and blockchain does not only guarantee the absence of tampering of the data but also allows the real-time validation prior to commitment to permanent ledgers. Further development will expand this architecture to cross-border organic trade, cross-border federated learning of farm-specific models, and combine it with decentralized identifiers (DIDs) to ensure consumer trust.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".