The partners behind new detection.

• Whitefly detection
• Breeding acceleration
• Greenhouse-trial cameras

Joint project to improve pest management and plant breeding efficiency using AI-based phenotyping. Cameras installed in a research greenhouse in Bleiswijk, Netherlands continuously photograph tomato canopies — Croptimus then detects whiteflies and quantifies their impact on each plant. The data accelerates pest-resistance assessment cycles for breeders, replacing manual counts that previously took weeks.

• 3 megacages + open field
• Seed-disease detection
• Pollination tracking

Bayer Crop Science (BCS ID) teamed with Fermata to improve carrot and onion seed production. After a successful 2023 pilot, the project expanded in 2024 with advanced cameras installed across three megacages and an open field. The initiative targets seed-transmitted diseases, pollination tracking, and pest control — ensuring optimal crop health and consistent high-quality seed output.

• Weather + satellite fusion
• Personalized fertilization
• Single grower workflow

Combining Croptimus™ computer vision with Yield Computer's yieldsApp agronomic platform, this collaboration fuses local weather data, crop models, satellite imagery, and field-level detections to deliver personalized fertilization and pesticide recommendations. The joint solution covers detection, decision support, and treatment planning in a single workflow for the grower.

• 700 strains
• 100-day trial
• Resistance grading 1–7

A 100-day collaborative project inoculating over 700 tomato strains with ToBRFV, planted across three greenhouse locations. Croptimus collects the imagery used to train a dedicated ToBRFV detection model, and grades each strain against Bayer's resistance scale (1–7). The output is both a production detection model and validated resistance data for breeders.

• 30 plants
• 3-month cycles
• Early-detection models

Internal R&D project running 30 tomato plants in controlled-environment rooms, artificially inoculated with target pests and diseases over a three-month window. The collected imagery seeds the next early-detection models for classes not yet present at customer sites — letting growers identify and treat emerging threats before they spread into commercial production.