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.
Technologies used
Python
PyTorch
PyTorch Lightning
Pandas
OpenCV
React Native
Docker
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
Challenge 01
Inconsistent Human Grading
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.
#1
Challenge 02
Eliminating lighting glare in steak analysis
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.
#2
Challenge 03
Rapid and Reliable Feedback Required
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.
#3
Our approach
Understanding Client Needs
Azati conducted a discovery call with the client to understand requirements and explore current market solutions for marbling assessment, creating a tailored approach for the project.
Training a CNN Model for Marbling Detection
Azati trained a CNN using a client-provided dataset, ensuring the model could accurately detect and classify marbling levels across various steak images.
Mobile App Development and Iterative Refinement
The mobile application was designed and developed as an MVP, allowing users to capture photos and assess marbling levels. User feedback led to three iterations, further enhancing functionality and user experience.
Enabling Seamless Model Updates
The app was built to allow easy updates of the AI model without requiring a full rebuild, ensuring scalability and maintainability.
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Schedule a callSolution
01
CNN-Powered Image Analysis
Leverages a convolutional neural network trained on client-provided datasets to automatically detect and classify steak marbling levels from images. Handles variations in lighting, steak orientation, and fat distribution to ensure accurate grading.
Key capabilities:
- Automatic marbling detection and classification
- Handles diverse lighting and steak orientations
- High accuracy comparable to human graders
02
User-Friendly Mobile Interface
Provides an intuitive mobile application interface for capturing steak images and receiving immediate grading results. Designed for non-technical staff to operate efficiently without special training.
Key capabilities:
- Capture and process steak images easily
- Instant feedback on marbling grades
- Minimal training required for staff
03
Flexible AI Model Updates
Supports seamless updates of the CNN model without requiring a full app rebuild, allowing continuous improvement and adaptation to new grading standards or datasets.
Key capabilities:
- Incremental model updates without app redeployment
- Easy integration of new datasets
- Ensures long-term scalability and maintainability
Results & business impact
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.
