AI-Powered Egg Quality Assessment and Classification
Case:
Precision Cracked Eggshell Detection with Automation
Chicken eggs are a popular nutritional staple, but cracked eggs present a risk of salmonella contamination, even with thorough cooking. Robust eggshells serve as a vital protective barrier, permitting the passage of essential gases and moisture. To ensure safety and quality, inspecting eggshells for defects and grading them before distribution is crucial. Implementing automated AI-based systems can streamline this process, enhancing efficiency and minimizing the risk of compromised egg quality.
Challenge
Challenges in Accurate Eggshell Defect Prediction
Eggshell quality is typically evaluated based on the density of pores on the shell’s surface, classified into various levels by standard grading systems. However, the irregular distribution of pores and cracks on eggshells presents a significant challenge for predicting and detecting defects. The high-speed processing and specific angles required on production lines further complicate traditional automation efforts. Until recently, manual inspection was the primary method, although it proved to be inefficient.
Solution
Advanced Visual Inspection with VisionPrime
Leveraging deep learning, VisionPrime utilizes AI technology to identify and annotate eggshell defects in sample images, training the inspection system. The AI model then becomes adept at detecting pores and cracks on the eggshell surface, accurately categorizing eggs based on the trained criteria. This method ensures compliance with safety standards and enhances the overall value of the products through improved quality control.