Datasets:

DigiGreen
/

farmerchat-image-samples

@@ -27,31 +27,13 @@ A sample of farmer-submitted photographs from FarmerChat, an agricultural adviso
 used by smallholder farmers in India, Ethiopia, Kenya and Nigeria. Published by
 [Digital Green](https://www.digitalgreen.org).
-This release contains 5,946 records (5,814 distinct photographs; some
-photographs belong to more than one category, see below) drawn from 6 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.
-## Changes in this version
-This version replaces an earlier release that had three problems. Some images had never
-actually cleared the service's quality gate. A small number of off-topic images
-(screenshots, AI-generated pictures) had slipped through because the quality gate only
-checks blur/framing, not content — both fixed, see "How these images were selected" below.
-Third, the `livestock` category has been removed entirely. It was built from
-`livestock_signal`, a flag derived from the farmer's accompanying text, not from the
-photo — and on inspection, none of its images actually showed an animal (they were plant
-photos sent alongside a livestock-related question, or otherwise mismatched). The
-automated content check in step 2 below does not catch this: it verifies a photo is a
-real, on-topic agricultural image, not that it matches its assigned category, so these
-rows passed it cleanly despite being wrongly categorized. Rather than publish a category
-that doesn't reliably contain what it claims to, it's cut until we have a livestock-photo
-label that's actually image-derived. This is why the record count and category count both
-dropped again from the previous version of this fix.
 ## Loading
 ```python
@@ -69,16 +51,20 @@ 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.
 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.
 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.
 ## Categories
@@ -90,11 +76,18 @@ crops.
 | `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 |
 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`.
 ## Columns
 | Column | Description |

 used by smallholder farmers in India, Ethiopia, Kenya and Nigeria. Published by
 [Digital Green](https://www.digitalgreen.org).
+This release contains 6,096 records (5,964 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
 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
 | `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` | 150 | 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 |