Fry Assessment System Using Pixel Color Analysis
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Solution Overview
Problem
The manual process of scoring potato quality, particularly for French fries, is subjective and prone to variations due to environmental and personal factors, affecting the accuracy and consistency of fry color scores, which impact pricing and compliance with USDA standards.
Innovation Solution
A computerized imaging analysis system that acquires and processes color images of potato segments or products to determine scores based on pixel-level color data, using a camera and image analyzer to assign grades or scores by analyzing multiple color values and requirements, and displaying re-colored images with assigned scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual visual estimation is used for fry color scoring, then the process is simple and quick, but the scoring is subjective and inconsistent
Solution Approach 1:
The patent replaces the manual visual estimation process with an automated computerized imaging system. A camera captures images of French fries, and software algorithms automatically analyze pixel color values to determine fry color scores. This substitution eliminates human subjectivity and environmental factors affecting manual scoring, providing consistent and objective measurements while maintaining operational efficiency.
Solution Approach 2:
The system creates a digital copy of the physical French fries through photography. Instead of directly observing and scoring physical samples, the system analyzes digital images containing pixel data that represents the fry colors. This copying approach allows for repeated analysis without disturbing the samples and enables precise digital measurement of color properties.
2Measurement precision
If automated imaging analysis is used for fry color scoring, then objectivity and consistency are improved, but system complexity increases
Solution Approach 1:
The patent employs a universal color analysis algorithm that can evaluate multiple parameters (fry color scores, sugar content indicators, quality metrics) from a single image capture. The system is designed to handle various potato products and scoring requirements through configurable software parameters, reducing the need for multiple specialized devices while maintaining measurement precision.
Solution Approach 2:
The system incorporates automatic calibration and self-adjustment features. The software includes built-in reference standards and algorithms that automatically adjust for lighting conditions and camera variations, eliminating the need for complex manual calibration procedures. This self-service capability reduces operational complexity while maintaining scoring accuracy.
3Measurement precision
If multiple color values are analyzed per pixel, then scoring accuracy is improved, but processing time increases
Solution Approach 1:
The patent divides the image analysis into distinct processing stages: first identifying and segmenting individual fry objects from the background, then analyzing color values within each segmented region. This segmentation approach allows for efficient processing by focusing computational resources only on relevant areas, reducing overall processing time while maintaining accurate multi-parameter color analysis.
Solution Approach 2:
The system analyzes more color parameters than the minimum required for basic scoring. By evaluating multiple color values (R, G, B, and derived parameters like redness, greenness, yellowness) simultaneously, the system obtains comprehensive quality data in a single processing pass. This excessive analysis approach provides redundant information that can be used for multiple scoring purposes without requiring additional image captures or processing cycles.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method provides a more objective and consistent scoring system, reducing human error and environmental influences, enabling accurate determination of potato quality and compliance with USDA standards, thereby improving pricing and storage management.
Implementation Method 1
A color image of a potato segment is acquired using a camera
Data Source
AI summary
A fry assessment system (10) is disclosed and includes an image analyzer (12). The image analyzer (12) includes both a fry identification module (90) and a fry scoring module (100). A color image (70) is analyzed by the fry identification module (90) to identify all fries in the color image (70). Thereafter, the fry scoring module (100) determines a score for each identified fry in the color image (70). These determined scores may be used for any appropriate purpose, for instance for purposes of determining a selling price for associated potatoes, to monitor a condition of associated potatoes in a common storage area, or the like.