Why agriculture needs computer vision
Agricultural decisions — how much pesticide to apply, when to treat, which part of the crop needs attention — have traditionally been made from the agronomist's experience and manual sampling. That works, but it doesn't measure: it produces no auditable number that can be compared across seasons or used to reduce chemical use without risking yield.
Computer vision turns a photo of the crop into an objective data point — without a lab, without breaking the field team's workflow.
Real use case: spray coverage analysis with Spray Analyzer
Spray Analyzer, developed by PlusSeed (our parent company), measures the coverage percentage of an agricultural spray treatment using UV fluorescence: a treated leaf is photographed under a 365nm UV lamp, and the computer vision model counts the fluorescent pixels to calculate what percentage of the surface was actually covered.
- 10,000+ leaves measured in real production.
- Results in seconds per photo, exportable to Excel/CSV.
- No lab, no spray cards — just a standard phone camera and a UV lamp.
- In real use with Almagrícola, a farming operation in Bogotá, Colombia.
You can see the system in action and read the full case on our case study page, or directly at sprayanalyzer.com.
Other agtech use cases we evaluate
Spray Analyzer is our verified case; these are additional computer vision applications in agriculture that we evaluate case by case, without promising a result before assessment:
- Early detection of pests and foliar disease from imagery.
- Fruit counting and yield estimation before harvest.
- Water stress monitoring through color and near-infrared analysis.
Related services
The agtech sector relies mainly on our computer vision service, combined with AI process automation to go from data to decision without manual intervention.