Explore ground truth for agriculture

We cultivate progress in modern agriculture. Our platform offers precise ground truth annotation tailored to diverse agricultural application fields.

We nurture innovation in agriculture

Discover the future of agriculture with our ground truth annotation platform and automation technology. We are reshaping farming by delivering precise ground truth data at scale. Join us in pioneering efficiency and sustainability in agriculture.

Ground Truth use cases within agriculture

From crop management to smart spraying, understand.ai effectively addresses all relevant use cases.

2D Driver Monitoring System

Automatic weeding / harvesting

Automated machinery handles weed removal or crop harvesting without human intervention, leveraging ground truth data for precise task execution, reducing labor and enhancing productivity.

2D Driver Monitoring System

Heavy machinery

Facilitating efficient operations and minimizing crop and equipment damage, ground truth mapping of fields and terrain enhances the performance and safety of heavy machinery.

2D Driver Monitoring System

Smart spraying

Utilizing drones or robotic sprayers, targeted pesticide or fertilizer application is optimized based on real-time crop health data, minimizing environmental impact and operational costs with ground truth insights.

2D Driver Monitoring System

Crop management

Improving resource efficiency and promoting sustainable farming practices, ground truth data informs optimized decision-making in irrigation, fertilization, and disease control.

    Reference Case: AgTech

    Explore this following reference case to gain deeper insight of what a successful collaboration with us could look like.

    In this reference case, we successfully delivered automated 2D bounding box annotation with necessary attributes to help our customer to developed system which can potentially be used in yield estimation of apple orchard and determine the maturity of apples.
    A system was developed for detecting physiologically mature red apples and an algorithm was developed to extract the feature color values from 5 defined areas.

    The Challenge

    • Consistent object tracking between the different cameras
    • Creating efficient, clear and automation friendly labeling specifications

    Key Facts

    • With numerous varieties and a short picking season, it was challenging to maintain pace.
    • Implemented a unique approach, blending bounding boxes and polygon annotation to enable the use of the same dataset across use cases
    • Professional customer consulting in terms of designing the whole project and shaping the labeling specs
    • Fully managed annotation service - 2D bounding boxes

    Key Achievements

    • Achieved annotation quality target of 99%
    • The client was able to leverage a single dataset across multiple use cases to customize and refine their model to their product specifications.
    • Solved a series of data quality and calibration problems