All Technologies Used
Motivation
The client, a major player in the U.S. Agricultural sector, faced inconsistent and time-consuming steak grading, with human evaluators prone to subjective errors. Azati developed a mobile app using a CNN model to automate marbling assessment, providing fast, consistent, and objective results. The system handles variable lighting conditions, subtle marbling differences, and allows seamless updates, enabling the client to improve quality control, reduce grading time, and maintain standardization across production.
Main Challenges
Manual steak grading was highly subjective, with different evaluators often assigning different marbling scores to the same piece of meat. This inconsistency slowed down production decisions and made it difficult to maintain standardized quality across batches, impacting customer satisfaction and operational efficiency.
Bright refrigeration lights caused glare on the surface of the steaks, which could be misinterpreted by AI models as fat, leading to inaccurate marbling grades. The system had to incorporate preprocessing techniques and controlled imaging conditions to minimize these errors and ensure reliable analysis.
The client needed near-instantaneous grading results to keep production lines moving efficiently. Achieving both high accuracy and fast feedback was challenging, as the model had to process images quickly without compromising grading precision, while the mobile app needed to deliver results seamlessly to users on the floor.
Our Approach
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CNN-Powered Image Analysis
- Automatic marbling detection and classification
- Handles diverse lighting and steak orientations
- High accuracy comparable to human graders
User-Friendly Mobile Interface
- Capture and process steak images easily
- Instant feedback on marbling grades
- Minimal training required for staff
Flexible AI Model Updates
- Incremental model updates without app redeployment
- Easy integration of new datasets
- Ensures long-term scalability and maintainability
Business Value
High-Accuracy Grading: The CNN model provides reliable and objective marbling scores, achieving 98% accuracy compared to traditional human evaluation, ensuring consistent quality assessment across all steaks.
Reduced Manual Effort: The mobile app automates the previously manual grading process, saving significant time for staff and reducing human error in steak evaluation.
Improved Operational Efficiency: Faster grading workflows allow employees to focus on other valuable tasks, streamlining meat selection and processing pipelines in the client’s operations.
Flexible and Scalable Solution: The app supports incremental AI model updates and iterative improvements, ensuring the solution remains accurate and adaptable as grading standards or datasets evolve.
Enhanced User Experience: An intuitive mobile interface allows non-technical staff to use the system efficiently, with instant feedback and seamless integration into daily operations.
