README.md

---
license: cc-by-4.0
language:
- en
- hi
- te
- am
- om
task_categories:
- image-classification
- tabular-classification
pretty_name: FarmerChat Crop Image Samples
size_categories:
- 1K<n<10K
tags:
- agriculture
- plant-disease
- crop-health
- india
- africa
- multilingual
---

# FarmerChat Crop Image Samples

A sample of farmer-submitted photographs from FarmerChat, an agricultural advisory service
used by smallholder farmers in India, Ethiopia, Kenya and Nigeria. Published by
[Digital Green](https://www.digitalgreen.org).

This release contains 6,089 records (5,957 distinct photographs; some
photographs belong to more than one category, see below) drawn from 7 categories
representing different outcomes of an automated crop diagnosis pipeline, sampled
across country, month, crop and diagnosis where each applies.

Submissions span 2025-08 to 2026-07.

## Loading

```python
from datasets import load_dataset

ds = load_dataset("DigiGreen/farmerchat-image-samples", split="train")
```

Or load `metadata.csv` directly and read images from the `images/` folder using its
`file_name` column.

## How these images were selected

Every photograph in this release cleared two independent screens.

1. **The service's own image-quality gate.** Submissions that the gate rejected (blur,
   lighting, framing, no plant detected) are not included. This restricts the release to
   submissions from the period the gate has been running. The `livestock` category is the
   exception: the gate looks for a plant as the dominant subject, so it rejects genuine
   livestock photos by design. Those images are included based on screen 2 instead.
2. **An automated content check.** Each remaining image was reviewed by a vision-language
   model and kept only if it is genuinely agricultural, is a plausible diagnostic
   submission rather than an accidental or off-topic one, and is an original photograph
   rather than a screenshot, a stock or template graphic, or an AI-generated image. For the
   `livestock` category, this same model's description of the photo is what confirms it
   actually shows an animal, rather than relying on the farmer's text alone.

Farmers do send off-topic images to the service — screenshots, photos of documents, pets,
food, AI-generated pictures. Those are a real and interesting property of the traffic, but
they are filtered out here so this dataset is what it claims to be: real photographs of
crops and livestock.

## Categories

| Category | Records | What it represents |
|---|---:|---|
| `multimodal_organic_text` | 999 | The photograph was accompanied by farmer-written text (not a repeated quick-reply template) |
| `low_confidence_diagnosis` | 995 | A diagnosis was returned, but with lower model confidence |
| `diagnosis_returned` | 991 | A crop was identified and a specific disease/pest diagnosis was returned |
| `crop_identified_diagnosis_unresolved` | 991 | A crop was identified but the health outcome was not determined |
| `healthy_crop` | 987 | A crop was identified and assessed as healthy |
| `crop_unresolved` | 983 | No crop could be identified in the photograph |
| `livestock` | 143 | The photograph shows an animal rather than a crop |

An image can belong to more than one category (for example, a low-confidence diagnosis
photo that also arrived with organic farmer text). Each membership is its own row in
`metadata.csv`, sharing the same `file_name`.

`livestock` rows leave `crop` and `diagnosis` as `not_applicable` or `no_crop_detected` —
the pipeline they were sampled from is built to diagnose crops, not animals. `farmer_query`
/ `farmer_query_en` are also left blank on these rows unless the farmer typed their own
text beyond the standard "what's wrong with my crop" quick-reply, since that quick-reply
doesn't describe the photo.

## Columns

| Column | Description |
|---|---|
| `file_name` | Relative path to the image file |
| `image_id` | Stable per-image identifier |
| `category` | One of the categories above |
| `country` | Country the submission came from |
| `month` | Year-month the photo was submitted (YYYY-MM) |
| `crop` | Crop identified in the photo, where resolved. Normalized to a standard set of names (synonyms merged, casing standardized) |
| `diagnosis` | Diagnosis identified, where resolved. Normalized the same way — not a pass-through of any single service's raw label |
| `query_present` | Whether the farmer sent text alongside the photo |
| `farmer_query` | Farmer's question, in the original language, PII-redacted |
| `farmer_query_en` | The same question translated to English, PII-redacted |

## Personal data

Every photograph was automatically screened for personal data (faces, printed personal
documents, GPS location overlays, and similar) before being included here; anything flagged
was excluded from this release.

Accompanying farmer text (`farmer_query` / `farmer_query_en`) was separately checked with an
automated language-model pass; any personal names, phone numbers, email addresses or physical
addresses found were replaced with `[REDACTED]`.

Automated screening reduces but does not eliminate the chance of residual personal content.
This dataset should not be treated as guaranteed free of it. In particular, some photographs
carry a camera watermark or timestamp burnt into the image by the phone that took them.

## Limitations

Diagnoses and crop/health labels are outputs of an automated pipeline, not verified expert
labels. They reflect what the system determined, not necessarily agronomic ground truth.

The content check in step 2 above is itself an automated model judgement, so it will have
made some mistakes in both directions.

## License

CC BY 4.0.

## Contact

lakshmi@digitalgreen.org