Register for free to
view the full innovation profile.
We have built complex software that can generate synthetic images and 100% pixel-perfect labelling data for every object. We use these to train AI models for agriculture.
About
AgriSynth is the first to market in agricultural AI innovation with its revolutionary synthetic image datasets designed to enhance the capabilities of AI-driven agricultural vision systems. Their systems employ advanced synthetic imaging techniques to create highly detailed and realistic datasets that can be used to train machine learning models more effectively. This innovation addresses a crucial gap in agricultural AI, generating large datasets of known scope and variance and ensuring 100% pixel-perfect object labelling within the images. We can enable robotics companies, agricultural R&D, Vertical Farming, grain quality assessment and many other market segments to move forward with AI. AgriSynth's commitment to leveraging cutting-edge AI and synthetic data generation techniques distinguishes it as a World Leader in the agricultural tech industry.
Key Benefits
The benefits of our innovation encompass enhancing AI model training efficiency and effectiveness. Our software technology revolutionizes how AI systems perceive and interpret visual data by using synthetic images that can be tailored to include a diverse range of scenarios and conditions not commonly found in traditional datasets. Such comprehensive training material allows AI models to achieve higher accuracy and robustness, particularly in applications where real-world data is scarce, costly, or challenging to capture. Furthermore, we enable rapid prototyping and testing of AI models as synthetic data can be generated on-demand, significantly reducing the time and resources typically required for gathering and labelling vast amounts of images. This innovation also promotes scalability in AI training processes, as it facilitates the easy generation of varied data sets to train models for complex and dynamic environments.
Applications
There are a wide variety of applications spanning several agriculture, horticulture, forestry and other major segments. In agriculture, it can be used to create high-fidelity, synthetic images of crops under various conditions to train machine learning models, improving automated pest detection and crop analysis without the need for extensive field data collection. In the grain trading industry, we can train AI models to identify grain quality issues throughout the grain trading process enabling pricing to be more realistically set against grain quality. This has the potential to change the grain trading on a global scale.