Data quality can be a major challenge for organizations, as it can affect the accuracy and reliability of analytics results, as well as slow down efficiency. Machine learning provides a suitable solution to this challenge, as its use improves data quality and consistency, leading to more accurate and reliable analyses.
One example is the use of machine learning in image processing: when images are of poor quality, AI can improve them by detecting patterns in the data and filling in missing details.
In addition, AI helps monitor and improve data quality. For example, machine learning models can help automatically detect and correct erroneous data. The model can learn from existing data and make predictions about which data are likely to be in error. These data can then be corrected automatically or manually.
Improve data quality and consistency.
Provide more accurate and reliable analysis.
Increase predictability and quality of innovations.
Reduce labor time and costs by automating manual processes and increasing efficiency.
With the AI solution ArtificialVet®, animal welfare indicators and slaughter findings are specified and standardized via exemplary camera images.
Our AI application BoxInspector® provides automated quality assurance through image recognition with optimal recognition performance.
The AI solution HookTracing® offers the visual identification of individual animals via camera images of the Eurohooks (DIN 250).
In our AI potential workshop, we identify concrete use cases for AI in your company.
LivestockGuardian: Our AI-powered sensor data analysis for automated and continuous high animal activity detection.
With the AI solution MeatVision, (disassembled) products in returnable transport packaging and EURO boxes are automatically recognized.
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