Blockchain-Enabled Rental System for Agricultural Asset Management Using Hyperledger Fabric
Bibliographic record
Abstract
In the digital age, the agricultural industry faces unique challenges, including the efficient management and maintenance of farm vehicles and tools. This paper introduces a blockchain-based agricultural vehicle and tool rental system leveraging Hyperledger Fabric. By utilizing blockchain technology, the system ensures immutability, transparency, and trust among all parties involved. The primary focus is on creating a decentralized and secure platform that not only facilitates the rental process but also introduces next-generation farm vehicle and tool maintenance capabilities. Throughout the development process, we faced and overcame significant challenges, including the need for a custom-tailored blockchain solution to meet the unique requirements of agricultural asset management. The proposed solution enhances traceability and accountability, thereby reducing fraudulent activities and improving overall operational efficiency. A working demo of the system is available on GitHub, providing an open-source resource for researchers and developers. This accessibility enables further exploration and adaptation of the system across various fields, from supply chain management to peer-to-peer marketplaces. The blockchain’s immutability aspect serves as a cornerstone for building trust in digital transactions, offering potential applications in diverse sectors such as finance, healthcare, and government services. The implementation details, innovative approaches to technical obstacles, and practical implications of our solutions are thoroughly discussed, providing valuable insights for researchers and practitioners in blockchain technology and agricultural systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".