IndiVillage
Aerial view of a soybean canopy with a visible weed pressure patch, showing the imagery scale and detail behind Taranis' 460-species annotation taxonomy.
Case Study · AgTech
99.4%
Sustained accuracy across 4.5M aerial frames, four seasons, one team.
Taranis · 4 seasons delivered · 50% YoY
02 · The challenge
Millions of aerial frames a season. 460+ weed species across nine global regions. Previous vendors could move the volume — they couldn’t hold the taxonomy across seasons.
Each frame demands species-level identification with sub-1% misclassification — early enough in the growth cycle to drive prescription decisions.
Annotator churn at previous vendors meant the same taxonomy got re-learned every quarter. Accuracy degraded under load.
03 · How we did it
A taxonomy-first programme with the same team across four growing seasons.
01
Taxonomy
Built the 460-species weed ontology with Taranis’ agronomy team. Boundary rules for ambiguous early-stage growth. Gold set curated in week one.
Schema-first · 460 species ontology
02
Multi-pass review
Three-level QC across every batch — L1 annotator, L2 reviewer, L3 QA specialist. Disagreement-resolution protocol for ambiguous frames.
Three-pass review · disagreement protocol
03
Proactive tooling
Internal dashboards built before the customer asked. Inter-rater agreement, throughput, and seasonal drift flagged automatically.
Proactive QA dashboards · drift detection
04
Capacity scaling
50% annual capacity growth without quality drop-off. Same delivery lead, expanded annotator team — knowledge compounded inside the team.
50% YoY capacity growth · zero quality drop
04 · The outcome
The accuracy held because the team didn’t change. A 70-person expert team with domain depth across 30 crops — same annotators, same QA leads, year over year. Knowledge compounded inside the team.
Taranis scaled annotation capacity by 50% year-on-year without losing precision. The proactive QA dashboards we built — not because Taranis specified them, but because we knew proactive QA was the surest way to drive accuracy and performance on the programme — flagged drift before it reached production.
05 · In their words
IndiVillage is the only one of our suppliers doing their own internal QA. The outcome is superior to the rest.
Idan Harary, Director of Ag at Taranis
Idan Harary · Director of Ag · Taranis
06 · The numbers underneath
460+
Weed species in live taxonomy
4.5M+
Images annotated to date · sub-1% miss
3-level
QC review on every batch
08 · Work with us
Run a modality-specific audit.
100 frames. Your taxonomy. Your accuracy target. Returns in 48 hours — with a programme recommendation.
Run AgTech audit
08 · Work with us
Run an annotation audit on your data.
Send us 100 frames in any modality — image, video, LiDAR, audio, text. We'll return annotated output, an accuracy benchmark, and a programme recommendation in 48 hours.