Grain Recognition via Dual-Model Image Analysis
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Solution Overview
Problem
Current technologies lack an effective method for automatically recognizing grain types and varieties in cooking devices, which hinders user convenience and interaction, especially when users are unsure of the type or variety of grain they are using.
Innovation Solution
A method and device that utilize image recognition models to differentiate between grain types and varieties by processing image data through feature enhancement, data enhancement, and learning, allowing for automatic recognition and determination of cooking modes without manual user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual rice type selection mode is used, then user can select cooking mode, but user experience deteriorates when user does not know rice type
Solution Approach 1:
The system performs automatic grain recognition and cooking mode selection without requiring user input. The control unit automatically captures images, processes them through recognition models, and determines cooking parameters, allowing the system to serve itself rather than requiring user knowledge or manual selection.
Solution Approach 2:
The patent replaces manual user selection (mechanical interaction) with automated image recognition and AI processing. Instead of requiring users to physically select or input rice type information, the system uses optical imaging and computational algorithms to automatically identify grain characteristics and determine appropriate cooking modes.
2Adaptability or versatility
If automatic grain recognition is implemented, then user experience improves, but device complexity increases
Solution Approach 1:
The control unit serves multiple functions: it manages cooking operations, controls image capture, processes recognition models, and determines cooking parameters. By making the control unit multi-functional, the patent avoids adding separate dedicated components for each function, thereby reducing overall system complexity while achieving automatic grain recognition.
Solution Approach 2:
The patent combines the image processing unit, recognition model, and cooking control functions into an integrated system. The control unit merges multiple functionalities (cooking control, image acquisition, image processing, and parameter determination) into a single coordinated system, reducing the number of separate components and simplifying the overall device structure.
3Measurement precision
If multiple recognition models are used for grain type and variety, then recognition accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary image processing steps including brightness recognition, clipping, feature enhancement, and data enhancement before applying recognition models. By preparing the image data in advance with enhanced features and multiple augmented versions, the recognition models can process more efficiently and accurately without requiring multiple sequential passes through the entire pipeline.
Solution Approach 2:
The patent divides the recognition process into distinct stages: image acquisition, brightness recognition, clipping, feature enhancement, data enhancement, and finally recognition model application. This segmentation allows each stage to be optimized independently, with parallel processing possible in the enhancement stages, reducing overall processing time while maintaining high accuracy through specialized processing at each step.
Data Source
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AI summary
A grain identification method and device, and a computer storage medium. The method comprises: obtaining first image data comprising grain undergoing identification (101); obtaining a first identification result on the basis of the first image data and a first identification model, and obtaining a second identification result on the basis of the first image data and a second identification model (102), the first identification result indicating the type of the grain undergoing identification, and the second identification result indicating a species to which the grain undergoing identification belongs; and determining, on the basis of the first identification result and the second identification result, information about the grain undergoing identification (103).