Eco-Chain: A Closed-Loop Multimodal AI and Consortium Blockchain Framework for Verifiable Plant Disease Diagnosis and Sustainable Adaptive Agriculture
Fuente:
Zenodo
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Recurso digital |
| Published: |
Zenodo
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866901719961567232 |
|---|---|
| author | Sania Parkar Utsav Pandey |
| author_facet | Sania Parkar Utsav Pandey |
| contents | Global food security is imperiled by a 40% annual loss in crop production due to pests and diseases, costing the global economy over $220 billion annually. While Deep Learning (DL) has achieved >95% accuracy in image-based disease classification, current solutions suffer from "contextual blindness" and a lack of accountability in treatment outcomes. This paper proposes Eco-Chain, a novel ecosystem integrating Multimodal Vision-Language Transformers (VLT) and a Consortium Blockchain. Unlike static diagnostic tools, Eco-Chain generates time-bound Care Regimens and enforces a closed-loop feedback mechanism. Farmer feedback is analyzed via Natural Language Processing (NLP) to calculate a Regimen Success Score (RSS), recorded on an immutable ledger. This system incentivizes "Proof of Care" through tokenomics, creating a verified, global knowledge base that empowers plants to adapt to climate change, thereby securing human existence. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19185506 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Eco-Chain: A Closed-Loop Multimodal AI and Consortium Blockchain Framework for Verifiable Plant Disease Diagnosis and Sustainable Adaptive Agriculture Sania Parkar Utsav Pandey Multimodal Deep Learning Vision-Language Transformers Consortium Blockchain Smart Contracts Regimen Success Score (RSS) Sustainable Agriculture Tokenomics Global food security is imperiled by a 40% annual loss in crop production due to pests and diseases, costing the global economy over $220 billion annually. While Deep Learning (DL) has achieved >95% accuracy in image-based disease classification, current solutions suffer from "contextual blindness" and a lack of accountability in treatment outcomes. This paper proposes Eco-Chain, a novel ecosystem integrating Multimodal Vision-Language Transformers (VLT) and a Consortium Blockchain. Unlike static diagnostic tools, Eco-Chain generates time-bound Care Regimens and enforces a closed-loop feedback mechanism. Farmer feedback is analyzed via Natural Language Processing (NLP) to calculate a Regimen Success Score (RSS), recorded on an immutable ledger. This system incentivizes "Proof of Care" through tokenomics, creating a verified, global knowledge base that empowers plants to adapt to climate change, thereby securing human existence. |
| title | Eco-Chain: A Closed-Loop Multimodal AI and Consortium Blockchain Framework for Verifiable Plant Disease Diagnosis and Sustainable Adaptive Agriculture |
| topic | Multimodal Deep Learning Vision-Language Transformers Consortium Blockchain Smart Contracts Regimen Success Score (RSS) Sustainable Agriculture Tokenomics |
| url | https://doi.org/10.5281/zenodo.19185506 |