The CVEDIA team supports agriculture and forestry technology companies in the development of tools, robots, and AI. CVEDIA is an industry leader in the use of custom synthetic data to accelerate the development of AI systems. Our tools and datasets are precisely built to minimize the gap between real and synthetic data for fluid algorithm training.

CVEDIA simulation tools are designed from the ground up to support and accelerate development from the earliest to the latest stages in the development process. Our SynCity simulation platform provides broadly representative data for new projects, and is used in data intensive classifier training to augment real world data, fill in gaps in coverage, and control for sampling bias. The result is rapidly produced, higher quality training sets that dramatically reduce field data collection, data storage, and labelling expenses.

Use Case

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Harvest Croo is a farming equipment company that came to our client MechaSpin with the plan to build an autonomous strawberry picker. They wanted to use LiDAR and RGB cameras to detect strawberries at each stage of growth, as well as identify equipment and people if they were to cross paths.

Detecting and avoiding humans is a common request that we receive. It’s difficult if not possible to gather sufficient real world data to train for this purpose – it’s dangerous, and any type of stand in dummy runs the risk of not being detected properly with certain movements, positions, or actions. Fortunately, it’s an area where synthetic data shines.


Creating LiDAR point cloud and RBG simulations, we were able to use 3D models of humans and create custom models for strawberry plants and farming equipment. Exporting scenarios that involved humans in different positions, walking, and moving, we could ensure that the autonomous picker would recognize humans when it was deployed. The training sets also worked to classify strawberries whether they were still green, developing, or fully ripe and red.

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Synthetic Datasets Train your machine learning model on validated synthetic data
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SynCity Use SynCity to develop, train, and validate your computer vision system with custom simulations
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Algorithm Training CVEDIA Algorithm Training Services
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    Reduce total project expense
    • SynCity reduces or eliminates the need for sensor deployment and field data
    • CVEDIA synthetic data dramatically reduces data collection and storage expenses
    • SynCity retrieves metadata and annotation from synthetic data - an expense you can absolve from your project
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    Minimize project length
    • Training autonomous applications on synthetic data dramatically reduces time requirements for gathering and organizing field data
    • Datasets can be created and exported in minutes in the browser with SynCity
    • CVEDIA uses assisted algorithm training by choosing which algorithms to apply to most quickly and completely validate your machine learning system
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    Validate your system effectively
    • Account for unmet field data scenarios that may be too difficult, expensive, or dangerous to explore, and simulate fog, sun glare, dust, sand, and water on your sensors
    • Choose from thousands of 3D models (or work with us to create your own) including infrastructure, natural elements, and humans to ensure your system behaves correctly during unexpected or edge case scenarios

Working with CVEDIA

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CVEDIA projects are developed by an experienced in-house team led by synthetic data industry leaders Arjan Wijnveer and Rodrigo Orph. Our team has been carefully vetted for over 10 years and includes AI veterans with backgrounds in machine learning R&D and large scale deployment.

CVEDIA works iteratively on client projects until satisfactory results are met. Our team has been met with praise from previous and ongoing clients across a range of industries, and we’re happy to be backed by FLIR Systems, the world’s leading thermal sensor producer. CVEDIA works to create custom environments and tool systems for each project, with varying levels of in-house service dependent on client needs.