Generalized Lorenz-Bayes Classifier for Plant Embryo Quality
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Solution Overview
Problem
Current methods for classifying plant embryos for germination and growth are labor-intensive, subjective, and inefficient, particularly in mass production settings, as they rely on visual evaluation and existing classification models fail to accurately and quickly distinguish embryo quality due to nonlinear boundaries and data complexities.
Innovation Solution
A generalized Lorenz-Bayes classifier method that uses digitized images and spectral data to classify plant embryos by calculating multi-dimensional density functions and comparing them to determine embryo quality, allowing for faster and more accurate classification, including the use of weights to adjust for misclassification costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual evaluation by skilled technicians is used to classify embryos, then classification accuracy can be maintained through expert judgment, but the process becomes highly labor-intensive and time-consuming
Solution Approach 1:
The patent replaces the mechanical visual evaluation system performed by skilled technicians with an automated image analysis system using computer vision and pattern recognition algorithms. The system captures images of embryos and automatically classifies them based on morphological features, eliminating the need for manual visual inspection while maintaining classification accuracy through sophisticated image processing techniques.
Solution Approach 2:
The patent creates a digital copy of the embryo visualization process by capturing images and processing them through computational algorithms. Instead of relying on human vision and judgment, the system uses digital image copies to extract morphological features and classify embryos automatically, enabling high-speed processing without sacrificing measurement precision.
2Productivity
If existing classification models are used to automate embryo classification, then productivity increases through automated processing, but accuracy decreases due to inability to handle nonlinear boundaries and data complexities
Solution Approach 1:
The patent transforms the classification approach by changing the parameters and methods used for analysis. Instead of using simple linear classification models, the system employs advanced image processing parameters including morphological feature extraction, texture analysis, and multi-dimensional feature space transformation. These parameter changes enable the automated system to capture nonlinear relationships in embryo data, maintaining high classification accuracy while achieving rapid automated processing.
3Reliability
If manual selection of embryos for germination is performed, then quality control can be maintained through expert judgment, but production bottleneck occurs during mass production
Solution Approach 1:
The patent replaces the manual embryo selection process with an automated image analysis system that maintains quality control through computational algorithms. The system rapidly processes images of multiple embryos, extracts morphological features, and classifies them according to germination potential, enabling mass production without the time loss associated with manual selection while maintaining reliable quality control through consistent automated criteria.
Data Source
AI summary
A method of classifying plant embryos according to their quality based on a general form of Lorenz-Bayes classifier is disclosed. First, image or spectral data of plant embryos of known quality are acquired, and the data are divided into two classes according to the embryos' known quality. Second, metrics are calculated from the acquired image or spectral data in each class. Third, multi-dimensional histograms of multiple metrics are prepared for both classes. Fourth, the difference or some other measure of comparison between the two multi-dimensional histograms is obtained. Fifth, image or spectral data of a plant embryo of unknown quality are obtained and metrics are calculated therefrom. Sixth, the embryo of unknown quality is assigned to a class based on its calculated metrics and the result of the comparison as calculated in the fourth step above.