field_service · manufacturing · workflow
super.AI computer vision reduces Triputra's fertilizer usage by 20% across 27 million trees
Triputra's Agriculture division needed to apply fertilizer efficiently across 27 million trees but had no way to process the millions of drone and satellite images they had collected — manual counting was impractical at this scale and the company had no prior ML experience.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Drone footage ingested
Drone video footage is taken as input to the computer vision pipeline.
Tools used
computer vision modelAPI
Outcome
Using a computer vision model across 27 million crop images, Triputra reduced fertilizer usage by 20% with no impact on crop yield and achieved 98% process automation (up from 0%).
Results
Time saved10 weeks
Volume20%
Cost replacedsignificant cost saving
Grounding & classification
Source type: production customer case
22 fields verified against source quotes.
computer visiondata extractionmetric backednamed customerproduction runtime claimedtools describedworkflow describedagricultureautomation ratecost reductionthroughput increaseproduction customer casefield servicequality assurancedocument to recordextract classify route