Green Coffee Bean Color Scoring for Low-Cost Defect Identification
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Solution Overview
Problem
Current methods for identifying green coffee beans are inefficient and costly, relying on manual selection or complex machine algorithms that require high computational power, making them unsuitable for small farmers or production teams, and lack effective use of inherent biological characteristics for identification.
Innovation Solution
A green coffee bean identification system and method utilizing an image capture device, memory, and processor to calculate average values and standard deviations of seed coat color grayscale distributions, enabling efficient identification of qualified beans through low-cost hardware like Raspberry Pi and Arduino, without additional data preprocessing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual selection method is used to identify green coffee beans, then the selection process is simple and accessible, but the identification efficiency is very low and time-consuming
Solution Approach 1:
The patent replaces manual mechanical selection with an automated image processing system that captures coffee bean images and automatically analyzes seed coat color characteristics. The system uses an image capture device to obtain images and a processor to calculate statistical parameters (average value and standard deviation) of seed coat color grayscale distributions, eliminating the need for manual visual inspection while significantly improving identification efficiency.
2Measurement precision
If complex machine algorithms are used to identify green coffee beans, then the identification accuracy is high, but the computational cost is high and the system is difficult to popularize to small farmers
Solution Approach 1:
The patent extracts only the essential statistical characteristics (average value and standard deviation) of seed coat color grayscale distributions from the complex image data, rather than using comprehensive complex algorithms. This extraction of key features maintains high identification accuracy while dramatically reducing computational complexity and hardware costs, making the system accessible to small farmers and production teams.
Solution Approach 2:
The patent transforms the identification problem from analyzing complex image patterns to comparing simple statistical parameters (average value and standard deviation of grayscale distributions). By changing the analysis parameters from detailed image features to aggregated statistical measures, the system achieves high accuracy with minimal computational resources.
3Loss of information
If traditional image preprocessing is performed on green coffee bean images, then comprehensive data is obtained for analysis, but the processing time is very long and the system becomes complex
Solution Approach 1:
The patent performs preliminary action by directly calculating the average value and standard deviation of seed coat color grayscale distributions from the raw image data without performing traditional preprocessing steps. This approach preserves the essential information needed for identification while eliminating time-consuming preprocessing operations, achieving both data completeness and processing efficiency.
Data Source
AI summary
A green coffee bean identification method includes obtaining an image of a green coffee bean by an image capture device, wherein the image includes pixels; reading an average value weight, a standard deviation weight, and a threshold value corresponding to the origin or the variety of green coffee beans from a memory; extracting three primary color grayscale values for each pixel from the image; calculating the average values and the standard deviations corresponding to the green coffee bean according to the approximate normal distributions of the three primary color grayscale value distributions; and respectively multiplying the average value and the standard deviation by the average value weight and the standard deviation weight to be then added together to calculate the total scoring value, wherein when the total scoring value is less than (respectively larger than) the threshold value, the green coffee bean is identified as a qualified (respectively defective) bean.