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Challenge

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.

Workflow AutomationHosted by Vera
Challenge brief

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.

Rules

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.

Delivery guide

How work is evaluated

Evaluation

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.

Submission format

Runnable source, pinned dependencies, setup commands, working interface, example outputs, recorded demo, tests, and action logs.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
4 scoring checks
Binary
1 pass or fail dimensions
Ordinal
3 scaled dimensions
Dimension 1

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.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 2

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.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 3

Review interface

Command-line processing script that reads mock audio transcripts and outputs tabular fertilizer demand manifests.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 4

Safe execution

Operates entirely on mock cooperative rosters and synthetic farmer IDs; does not collect actual land registry data.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

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.

Resources and assets

Reference links and supporting material

Dataset notes

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.

Evaluation checks
  • 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...
Configured evaluation evidence
  • 2 public reference cases
Configured runtime
  • 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

Policy Serving
  • 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.

Frequently Asked Questions about Build a Regional Fertilizer Demand Aggregator