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README.md

@@ -1,3 +1,106 @@
 ---
 license: cc-by-4.0
+language:
+- en
+- hi
+- te
+- am
+- om
+task_categories:
+- image-classification
+- tabular-classification
+pretty_name: FarmerChat Crop and Livestock Image Samples
+size_categories:
+- 1K<n<10K
+tags:
+- agriculture
+- plant-disease
+- crop-health
+- livestock
+- india
+- africa
+- multilingual
 ---
+
+# FarmerChat Crop and Livestock 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 8,000 records (7,906 distinct photographs; some photographs belong to
+more than one category, see below) drawn from 8 categories representing different outcomes of
+an automated crop/livestock diagnosis pipeline. Roughly 1,000 records per category, sampled
+across country, month, crop and diagnosis where each applies.
+
+## 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.
+
+## Categories
+
+| Category | What it represents |
+|---|---|
+| `diagnosis_returned` | A crop was identified and a specific disease/pest diagnosis was returned |
+| `crop_identified_diagnosis_unresolved` | A crop was identified but the health outcome was not determined |
+| `crop_unresolved` | No crop could be identified in the photograph |
+| `rejected_quality` | The photograph was rejected by an automated image-quality check (blur, lighting, framing, etc.) before diagnosis was attempted |
+| `healthy_crop` | A crop was identified and assessed as healthy |
+| `low_confidence_diagnosis` | A diagnosis was returned, but with lower model confidence |
+| `livestock` | The photograph was flagged as livestock-related rather than crop-related |
+| `multimodal_organic_text` | The photograph was accompanied by farmer-written text (not a repeated quick-reply template) |
+
+An image can belong to more than one category (for example, a livestock photo with a
+low-confidence diagnosis). Each membership is its own row in `metadata.csv`, sharing the same
+`file_name`.
+
+## Columns
+
+| Column | Description |
+|---|---|
+| `file_name` | Relative path to the image file |
+| `image_id` | Stable per-image identifier |
+| `category` | One of the 8 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 |
+| `diagnosis` | Diagnosis returned, where resolved |
+| `health_status` | `Healthy` / `Disease` / `Unknown` / `not_applicable` |
+| `confidence` | Reported confidence of the diagnosis, where applicable |
+| `livestock_signal` | `Yes` / `No` / `Unknown` — whether the submission was flagged as livestock-related |
+| `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.
+
+## 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.
+
+## License
+
+CC BY 4.0.
+
+## Contact
+
+lakshmi@digitalgreen.org