Food Production Efficiency and Risk Mitigation
on Pre-Batch Processes


  • The pre-batch step in food manufacturing involves manually measuring out small amounts of ingredients, which is prone to errors such as incorrect measurements, mislabelling and mixing mistakes.
  • This manual process lacks transparency and traceability, increasing the risk of product defects, allergen contamination, and regulatory non-compliance.
  • The complexity of multi-stage mixing further exacerbates these challenges. Food recalls are incredibly costly, with the average direct cost amounting to greater than $10m!


Food manufacturers strive to ensure precise ingredient measurement, mixing, and traceability to maintain product quality and meet regulatory standards. The primary objective in the pre-batch process is to eliminate human errors, enhance transparency, and achieve consistent product quality through accurate measurement and mixing procedures.


visionTrack offers targeted interventions to address specific challenges in the pre-batch process:

  • Source-to-Bag Verification: Use overhead cameras to observe and correlate barcode scanning with ingredient scooping. This ensures that the scanned ingredients match those measured into the bag – reducing the risk of mislabelling and incorrect measurements.
  • Bag-to-Palette Tracking: Vertical view cameras monitor the placement of each bag or box onto the palette, ensuring that the correct ingredients are placed on the appropriate palette, thereby minimizing mixing errors.
  • Independent Verification: Establish an independent verification process at the end of the pre-batch conveyor line, combining physical and digital checks to ensure accuracy and completeness of ingredient placement on palettes, reducing reliance on manual inspection.
  • Storage Area Monitoring: Introduce methods to track palettes and detect fallen bags in the storage area, enhancing visibility and minimizing the risk of misplaced or lost ingredients during storage and transport.
  • Hopper Loading Automation: Develop automated systems to read barcodes of ingredients as they are loaded into hoppers, ensuring accurate dispensing, and minimizing errors during the final stage of mixing.


Existing  factory cameras


Uses advanced computer vision algorithms and machine learning models to enable:

  • Real-time image capture and analysis for accurate identification of ingredients, reading labels/ identification, and human actions.
  • Potential for easy integration with existing systems for seamless data management and analysis, enhancing process visibility and control


  • Dramatically reduced instances of human errors in ingredient measurement and mixing.
  • Enhanced traceability throughout the pre-batch process, facilitating easier identification and resolution of issues.
  • Minimized risk of product defects, allergen contamination, and regulatory non-compliance.
  • Improved operational efficiency through automation and real-time monitoring.