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A computer vision platform for automated characterization of fruit and plant traits in real-world conditions.
From early crop development to the processing line.
Explore the system ↓
EPYCApp supports routine phenotyping across multiple crops and growing conditions, from fruits on the vine to harvest and post-harvest assessment.
Start with accessible RGB video or add synchronized depth when the project requires richer three-dimensional measurements.
The acquisition system adapts to the crop and operation—not the other way around.
Batch processing supports large trial datasets locally or on a server. Real-time processing delivers results during acquisition and connects directly with industrial workflows.
Count visible fruit, characterize fruit and plant traits, map spatial distribution, flowers, plant structure, and estimate future yield.
Open field / GreenhouseIntegrate image acquisition into harvesting operations to count, size, classify and estimate weight as the crop is collected.
Harvester / Field operationAnalyze harvested fruit on exposed surfaces, where full visibility supports detailed external and internal trait characterization.
Indoor / OutdoorDeploy synchronized video and real-time processing above conveyor systems to inspect every product, classify quality and feed automated industrial workflows.
Industrial facilities / Processing lineGeometric measurements from external appearance and internal sections.
Visible quality and features tailored to each crop and breeding objective.
EPYCApp extends phenotyping beyond the fruit to describe crop development and structure.
Turn visible fruit counts, size distribution and crop development into an early estimate of expected harvest.
Use repeatable image acquisition to identify and estimate visible symptoms across plants, rows or production areas.
Standard range reported in current ZNiR materials under stable conditions. Final performance depends on crop, visibility, sensor and project setup.
The installable smartphone version brings ZNiR's acquisition workflow directly to the field. Teams can record with familiar hardware while following the project-specific protocol that makes results repeatable.
