Coffee Bean Defect Sorting With Dual-Image AI and Air Ejection
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Solution Overview
Problem
Existing coffee bean sorting technologies fail to effectively identify and separate defective green beans, which affect the taste quality of specialty coffee, and are often bulky and noisy, making them unsuitable for small-scale operations.
Innovation Solution
A green bean defect sorter that uses an image learning algorithm to analyze top and bottom images of individual beans, sorted by air pressure into normal, defective, and indeterminable categories, with reduced equipment volume and noise, utilizing a compact design suitable for small-scale workplaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If traditional coffee bean sorting technologies are used, then sorting capability is provided, but equipment volume is large and noise is high
Solution Approach 1:
The patent implements nesting by placing the green bean rotary compartment inside the storing container, and positioning the imaging device within the compact structure. The disk lower plate with through-holes is nested within the disk upper plate, creating a space-efficient configuration that reduces overall equipment volume while maintaining sorting functionality
Solution Approach 2:
The patent utilizes vertical stacking of components (storing container above green bean rotary compartment, which is above sorted green bean storage) to transition from horizontal expansion to vertical arrangement. This dimensional change allows the system to maintain sorting capability while significantly reducing the equipment's footprint and volume
2Object-generated harmful factors
If traditional coffee bean sorting technologies are used, then sorting capability is provided, but noise level is high
Solution Approach 1:
The patent replaces traditional mechanical sorting mechanisms (which involve moving parts, motors, and physical manipulation) with an imaging-based detection system. The imaging device captures images of green beans, and an image learning algorithm processes these images to identify defects, eliminating noisy mechanical operations while maintaining accurate sorting capability
Solution Approach 2:
The patent uses air pressure through the air inlet hole to discharge sorted green beans from the green bean rotary compartment to the sorted green bean storage. This pneumatic mechanism replaces noisy mechanical ejection systems, reducing noise while effectively separating sorted beans based on defect detection results
3Measurement precision
If multiple cameras are used to capture top and bottom images, then defect detection accuracy is improved, but light interference between cameras occurs
Solution Approach 1:
The patent positions the two cameras at asymmetric locations: one camera is positioned to capture the top view of green beans, while the other camera captures the bottom view through the transparent support plate. This asymmetric arrangement ensures that the light paths of the two cameras do not intersect or interfere with each other, allowing simultaneous operation without light interference while maintaining high defect detection accuracy
4Measurement precision
If image learning algorithm is used for defect detection, then sorting accuracy is improved, but processing time increases
Solution Approach 1:
The patent implements preliminary action by pre-processing the captured images through normalization and feature extraction before applying the image learning algorithm. The system prepares the image data in advance by adjusting brightness, contrast, and geometric transformations, which reduces the computational burden during actual defect detection and minimizes processing time while maintaining high sorting accuracy
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
Accurately sorts green beans into quality categories, reducing equipment size and noise, enhancing taste consistency in specialty coffee production.
Implementation Method 1
an imaging device of two cameras configured to photograph the green bean kernel seated on the green bean rotary compartment and provide two images—top and bottom—of the photographed green bean kernel
Implementation Method 2
a green bean sorter configured to sort the seated green bean kernel by using air pressure according to a result decided by the processor
Data Source
AI summary
A bean defect sorter includes a container configured to store beans, a transfer guide configured to receive the beans from the container and to guide each bean in series, the transfer guide being configured to discharge the beans one by one, a rotary configured to rotate in a predetermined direction and to receive each of the beans discharged from the bean dispenser, an imaging device configured to provide a bean image of one or more of the beans, a processor including a learning algorithm that is configured to classify each bean as one of a normal bean, a defective bean, and an indeterminable bean, a bean sorter configured to discharge each bean from the rotary by using air pressure according to a classification result, a storage configured to store each of the normal bean, the defective bean, and the indeterminable bean, and a residual bean collector to process escaped beans.