native egg lot

SolVisionCase Study

Food and Beverage

Egg Quality Inspection and Classification

Case

Automated Cracked Eggshell Detection

Chicken eggs are widely consumed for their nutritional value, but cracked eggs pose a risk of salmonella contamination, even after thorough cooking. Robust eggshells act as a protective barrier, allowing the passage of essential gases and moisture. To ensure safety and quality, it is imperative to inspect eggshells for defects and grade them before distribution. Implementing automated AI-based systems can streamline this process, enhancing efficiency and minimizing the risk of compromised egg quality.

Challenge

Challenges in Predicting Eggshell Defects

Typically, eggshell quality is assessed based on the density of pores on the shell’s surface, categorized into different levels by common grading systems. However, the random occurrence of eggshell pores and cracks poses a challenge for predicting and detecting defective products. Eggs, processed at high speeds and specific angles on production lines, complicate traditional automation efforts. Until recently, manual inspection was the primary method, albeit with low efficiency.

Solution

Advanced Visual Inspection with SolVision

Utilizing deep learning, SolVision employs AI technology to identify and annotate eggshell defects in sample images, training the inspection system. The AI model is subsequently capable of detecting pores and cracks on the eggshell surface, accurately categorizing eggs based on the trained criteria. This approach ensures compliance with safety standards and enhances the overall value of the commodities through improved quality control.

Classification

Level 1 eggshell hole seam density detection

Level 1

Green

Level 2 eggshell hole seam density detection

Level 2

Blue

Level 3 eggshell hole seam density detection

Level 3

Pink

classification of eggs on a production line using AI
classification of eggs on a production line using AI

Outcome

AI-based visual inspection significantly improves eggshell quality assurance by accurately detecting defects, ensuring compliance with safety standards, and enhancing overall product quality.
Deep learning technology streamlines the grading process by automating the detection of eggshell pores and cracks, overcoming challenges in traditional automation.
SolVision’s vision inspection system replaces manual methods, offering higher efficiency in identifying and categorizing eggshell defects, reducing the risk of salmonella contamination, and ensuring greater food safety.
Related Posts

  • Intelligent Remote Facility Management

    Machine vision is utilized to perform Optical Character Recognition (OCR). This information is sent to the cloud to create an inspection report, allowing plant operators to easily monitor facility inspection through mobile devices.
  • green bottle lot

    AI Visual Inspection of Glass Bottles

    SolVision can be trained to recognize different types of stains and mildew. The software’s tool learns the location, color, and features of defects to identify them automatically in real-time on the cleaning production line

  • OCR Date Reading on Aluminum Bottle Caps

    SolVision’s OCR boosts food, beverage, and pharma product quality control, overcoming bottle cap printing challenges on high-speed production lines with AI.

  • Optimizing PCB Assembly Processes

    A PCB can have dozens of different parts and configurations, and subtle differences may not be obvious to the human eye. Manual inspections are easily susceptible to missed or inaccurate detections and are hard to optimize