Rice Grain Recognition System Using ML Segmentation
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Solution Overview
Problem
Current optical sorting systems for rice grains rely heavily on color analysis, which is inadequate for accurate classification of grains with small off-color spots and very short non-broken grains, leading to inefficiencies and inaccuracies in quality control.
Innovation Solution
An industrialized system for rice grain recognition that uses an optical capturing device to transmit images to a digital platform for analysis, employing a segmentation module, feature extractor, and classifier with machine learning structures to identify shape, color, and geometric parameters, and adjusts the discrimination threshold for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If color analysis is used for rice grain classification, then the classification process is simple and fast, but the accuracy is insufficient for grains with small off-color spots and very short non-broken grains
Solution Approach 1:
The patent segments the classification process into multiple specialized modules: a first classification unit for color-based classification and a second classification unit for shape-based classification. This segmentation allows each unit to specialize in specific defect types, improving overall accuracy without requiring a complete system overhaul. The segmentation enables parallel processing of different defect characteristics.
Solution Approach 2:
The patent transitions from purely color-based classification to multi-dimensional classification by incorporating shape parameters (length, width, area, perimeter) in addition to color parameters. This dimensional expansion allows the system to detect defects that color analysis alone cannot identify, such as very short non-broken grains and grains with small off-color spots, thereby resolving the accuracy limitation.
2Measurement precision
If advanced machine learning structures and threshold optimization are used, then classification accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-defining multiple classification thresholds (first threshold for color-based classification, second threshold for shape-based classification) and preparing multiple machine learning structures in advance. This allows the system to quickly select and apply appropriate classification criteria without performing extensive computations during real-time processing, thus reducing processing time while maintaining high accuracy.
Solution Approach 2:
The patent optimizes classification accuracy by dynamically adjusting parameters including discrimination thresholds and selecting from multiple machine learning structures (SVM, Random Forest, Neural Networks). The system changes parameters based on the specific classification task and defect characteristics, achieving high accuracy without requiring maximum computational resources for all cases.
3Reliability
If multiple classification units are used to improve accuracy, then classification reliability increases, but device complexity increases
Solution Approach 1:
The patent implements a dynamic classification system where the system can adaptively select which classification unit to use based on the input characteristics. The dynamic architecture allows the system to switch between color-based and shape-based classification, or combine both, depending on the defect type detected. This dynamic approach improves reliability by using the most appropriate classification method while avoiding unnecessary complexity.
Solution Approach 2:
The patent introduces an image processing unit as an intermediary that pre-processes input images before they reach the classification units. This intermediary performs essential operations like noise reduction, contrast enhancement, and feature extraction, which improves the quality of input data for both classification units. By placing this intermediary layer, the system achieves higher reliability without significantly increasing the complexity of the core classification logic.
Data Source
AI summary
An industrialized system and method for rice grain recognition. An optical image is taken and transmitted to a digital platform, wherein the system segments the optical image and extracts and/or measures appropriate grain features from the image describing different aspects of the grain. The image is processed by the system which includes a selector selecting different machine learning structures, applying the different machine learning structures to the extracted features for rice grain recognition, and selecting the best of the applied machine learning structures by a random sampling process. The selected best of the applied machine learning structures is further optimized by varying an appropriate threshold by a threshold trigger based on a confusion matrix comprising. An active learning structure based on the confusion matrix to the user. The system is retrained based on the feedback parameters of the feedback loop.