All Technologies Used
Motivation
The client, a major player in the U.S. Agricultural sector, needed a tool to standardize meat quality evaluation by grading steak marbling. Azati developed a solution to streamline this process by training a CNN model to detect subtle marbling differences and integrating it into a user-friendly mobile application, enhancing consistency and efficiency in steak grading.
Main Challenges
The client’s frequent business trips delayed responses and project approvals. To mitigate this, Azati transitioned to written communication supported by prototypes and clear examples, allowing faster decision-making and project alignment.
Steaks stored under bright refrigeration lights caused glare that AI models misinterpreted as fat, leading to overestimated marbling grades. To solve this, the client developed a custom ‘hood’ to block external lighting and ensure accurate image analysis.
Key Features
- CNN-Powered Image Analysis: Leverages Convolutional Neural Networks to evaluate steak marbling levels from photos.
- User-Friendly Mobile Interface: Provides an intuitive platform for users to capture images and receive instant quality grading.
- Model Update Flexibility: Allows seamless AI model updates without the need to rebuild the app.
- Iteration-Based Functionality Enhancement: Refined through multiple iterations based on real-world user feedback.
Our Approach
Project Impact
Azati’s solution delivered measurable improvements in steak grading efficiency and accuracy. The trained CNN model ensures consistent quality assessment, while the flexible app design supports long-term scalability and usability. The client can now provide reliable, objective grading standards with faster processing times, enhancing operational efficiency in meat evaluation.