Agritech operator · Agriculture
Farm intelligence platform
90% detection accuracy
Context
An agritech operator needed to automate crop monitoring and disease detection across large estates, reducing reliance on manual field inspections and enabling faster intervention.
Challenge
Manual scouting was slow and inconsistent, outbreaks were often caught too late, and existing tools lacked the vision capability to process drone and camera imagery at scale.
Approach
We built a pipeline that ingests drone and field-camera imagery, detects crop-health anomalies with trained segmentation models, and generates plain-language alerts through an LLM-powered reporting layer.
Solution
Detection models classify crop stress, disease indicators and pest damage; results feed a farm-management dashboard; and LLM-generated summaries explain findings clearly for farm managers.
Impact
90% detection accuracy on held-out test sets. Early intervention reduced crop loss in pilot fields, scout time fell significantly, and the same pipeline now covers multiple farm sites.