Agricultural Industry · Agriculture
Agritech services
90% detection accuracy
Context
An agritech operator needed to automate crop monitoring and disease detection across large farm estates, reducing reliance on manual field inspections and enabling faster intervention.
Challenge
Manual scouting was slow and inconsistent. Disease outbreaks were often caught too late, leading to significant yield loss. Existing software tools lacked the vision capabilities to process drone and camera imagery at scale.
Approach
We built a computer vision pipeline that ingests drone and field-camera imagery, detects crop health anomalies using trained segmentation models, and generates actionable alerts via an LLM-powered reporting layer.
Solution
PyTorch-based detection models classify crop stress, disease indicators, and pest damage. Results are aggregated into a farm management dashboard. LLM-generated summaries explain findings in plain language for farm managers.
Impact
90% detection accuracy on held-out test sets. Early intervention reduced crop loss in pilot fields. Field scout time cut significantly. The system now covers multiple farm sites with the same model pipeline.