This R&D project aims to improve pest management and plant breeding efficiency using AI-based phenotyping technologies to detect whiteflies and assess their impact on tomato plants. By installing a camera in a greenhouse in Bleiswijk, Netherlands, the project enhances data collection methods, speeding up and improving pest resistance assessments.

Bayer Crop Science (BCS ID) has teamed up with Fermata to improve carrot and onion seed production. Following a successful pilot in 2023, Fermata expanded the project in 2024 by installing advanced cameras in three megacages and an open field. The initiative targets critical challenges such as seed-transmitted diseases, pollination, and pest control, ensuring optimal crop health and efficient production of high-quality seeds.

Fermata equips agRE.tech’s autonomous robots with advanced AI and computer vision capabilities to automate crop screening and improve pest and disease detection. This collaboration addresses labor shortages and regulatory challenges by transforming crop data into precise, actionable insights, offering an eco-friendly solution that enhances profitability and combats food insecurity.

Combining Fermata’s Croptimus™ technology with yieldsApp’s agronomic management platform, this collaboration harnesses local weather data, crop models, satellite imagery, and computer vision to deliver personalized recommendations for fertilization and pesticide use. Together, we provide a comprehensive farm management solution, enhancing the detection and treatment of diseases and nutrient deficiencies.

This 100-day project involves inoculating over 700 tomato strains with the TBRF virus and planting each strain in three different greenhouse locations. The main objectives are to collect data for developing Fermata's TBRFv detection model and to grade the strains based on Bayer's resistance scale (1 to 7).

This project involves placing 30 tomato plants in controlled-environment rooms, where they are artificially infested or inoculated with various pests and diseases over a period of three months. The primary goal is to collect data to develop an early detection model for a wide range of pests and diseases, enabling growers to quickly identify and address issues before they spread.