Build a Regional Fertilizer Demand Aggregator
Process vernacular farmer inquiries regarding crop planting intentions. Aggregate required fertilizer varieties across mock taluk registries to generate cooperative procurement requests and simplified seasonal supply estimates using local AI models.
What you are building
The core problem, expected build, and operating context for this challenge.
Process vernacular farmer inquiries regarding crop planting intentions. Aggregate required fertilizer varieties across mock taluk registries to generate cooperative procurement requests and simplified seasonal supply estimates using local AI models.
Farmer producer organization coordinator
Ingest vernacular farmer voice transcripts detailing upcoming crop planting plans and field sizes.
Extract crop types, farm acreage, and requested fertilizer varieties using a local language model.
Aggregate required supply metrics across mock taluks for the upcoming planting season.
Generate a structured cooperative procurement manifest for regional agricultural suppliers.
Command-line processing script that reads mock audio transcripts and outputs tabular fertilizer demand manifests.
Curate mock vernacular voice transcripts of farmers describing their upcoming planting seasons and requested inputs. Starter examples below are synthetic. They are not downloaded records.
Bhashini documentation Adopts Bhashini pipeline standards for multilingual text processing and translation sequences in agricultural demand aggregation.
ollama, pandas, fastapi. Ollama securely runs localized small language models for vernacular extraction without relying on external cloud APIs. Local open-weights model capable of vernacular intent extraction and deterministic JSON formatting.
Submit runnable source, pinned dependencies, setup commands, a working interface, example outputs, and a recorded end-to-end demo. Include tests and an action log.
Accurately extracts crop type, acreage, and fertilizer type from vernacular text.
Aggregates total acreage and fertilizer demand by mock taluk ID.
Formats the final cooperative procurement manifest as a valid JSON or CSV payload.
```json [ { "input": { "transcript": "Agle mahine 5 acre me ganna lagana hai, urea chahiye.", "taluk_id": "TALUK-01", "lang": "hi", "farmer_id": "F-101" }, "expected_output": { "crop_type": "Sugarcane", "acreage": 5, "fertilizer_requested": "Urea", "aggregation_status": "LOGGED", "record_type": "simplified synthetic app record" } }, { "input": { "transcript": "Need DAP for 2 acres of wheat.", "taluk_id": "TALUK-02", "lang": "en", "farmer_id": "F-102" }, "expected_output": { "crop_type": "Wheat", "acreage": 2, "fertilizer_requested": "DAP", "aggregation_status": "LOGGED", "record_type": "simplified synthetic app record" } } ] ```
Operates entirely on mock cooperative rosters and synthetic farmer IDs; does not collect actual land registry data. Use mock external writes. Require human review before any real deployment.
Participation guide
Operates entirely on mock cooperative rosters and synthetic farmer IDs; does not collect actual land registry data. Use mock external writes and human review.
How work is evaluated
Accurately extracts crop type, acreage, and fertilizer type from vernacular text. Aggregates total acreage and fertilizer demand by mock taluk ID. Formats the final cooperative procurement manifest as a valid JSON or CSV payload.
Runnable source, pinned dependencies, setup commands, working interface, example outputs, recorded demo, tests, and action logs.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Complete workflow
Ingest vernacular farmer voice transcripts detailing upcoming crop planting plans and field sizes. Extract crop types, farm acreage, and requested fertilizer varieties using a local language model. Aggregate required supply metrics across mock taluks for the upcoming planting season. Generate a structured cooperative procurement manifest for regional agricultural suppliers.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Correctness and evidence
Accurately extracts crop type, acreage, and fertilizer type from vernacular text. Aggregates total acreage and fertilizer demand by mock taluk ID. Formats the final cooperative procurement manifest as a valid JSON or CSV payload.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Review interface
Command-line processing script that reads mock audio transcripts and outputs tabular fertilizer demand manifests.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Safe execution
Operates entirely on mock cooperative rosters and synthetic farmer IDs; does not collect actual land registry data.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Ingest vernacular farmer voice transcripts detailing upcoming crop planting plans and field sizes.
Extract crop types, farm acreage, and requested fertilizer varieties using a local language model.
Aggregate required supply metrics across mock taluks for the upcoming planting season.
Generate a structured cooperative procurement manifest for regional agricultural suppliers.
Reference links and supporting material
Curate mock vernacular voice transcripts of farmers describing their upcoming planting seasons and requested inputs. Supplied starter examples are synthetic, not source extracts.
Challenge setup
Configuration summary for this challenge.
Challenge input
Input details are not configured.
Ollama
Local LLM runtime for offline inference.
Evaluated output
No output checks are configured.
- Ingest vernacular farmer voice transcripts detailing upcoming crop planting plans and field sizes. Extract...
- Accurately extracts crop type, acreage, and fertilizer type from vernacular text. Aggregates total acreage...
- Command-line processing script that reads mock audio transcripts and outputs tabular fertilizer demand mani...
- 2 public reference cases
- 1 configured tool
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- Ollama · Optional
Evaluation contract
- Complete workflow · Weight 40
- Correctness and evidence · Weight 30
- Review interface · Weight 20
- Safe execution · Weight 10
Reference cases
- 2 public reference cases
Workflow tasks
No workflow tasks are configured.
Recipe freeze marker
No recipe freeze time is recorded.