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.
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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%).
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Frequently asked questions
What did this team achieve with this AI workflow?
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%).
What tools did this team use?
computer vision model, API.
What results were reported?
Fertilizer usage reduction: 20%; Process automation rate: 98%; Crop images processed: 27 million; Deployment timeline: 10 weeks (source-reported, not independently verified).
How is this field service AI workflow structured?
Drone footage ingested → Computer vision tree identification → Fruit yield counting → Automated tree database created → Per-tree fertilization map output.
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