Seed Sorting by Neural Imaging for Stress Resistance Classification
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Solution Overview
Problem
Current seed sorting technologies fail to accurately differentiate between stress-resistant and non-stress-resistant seeds, relying on visual and physical properties that are not always reliable, and often require destructive testing or time-consuming DNA analysis.
Innovation Solution
A system utilizing a neural network that classifies seeds based on images captured by imaging sensors, computing classification categories such as stress-resistant or non-stress-resistant by extracting and weighing visual and physical features, allowing for automated sorting without destructive testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If visual and physical properties are used for seed classification, then the sorting process is simple and fast, but the classification accuracy is insufficient and cannot reliably differentiate stress-resistant seeds
Solution Approach 1:
The patent transforms the classification approach from using basic visual and physical properties to using spectral parameters obtained through spectroscopic analysis. This parameter transformation enables the system to detect stress-resistant traits that are not visible through conventional inspection, thereby improving classification accuracy while maintaining automated sorting capability
Solution Approach 2:
The patent replaces traditional mechanical or manual sorting methods with an optical-spectral analysis system. By using imaging sensors and spectral analysis to detect seed properties, the system achieves both high-speed automated operation and improved accuracy in identifying stress-resistant seeds without relying on simple visual inspection
2Measurement precision
If destructive testing or DNA analysis is performed to accurately identify stress-resistant seeds, then classification accuracy improves, but testing time and complexity increase significantly
Solution Approach 1:
The patent creates an optical-spectral copy or fingerprint of the seed that contains information about its stress-resistant traits. By analyzing the spectral signature rather than performing destructive DNA analysis, the system obtains accurate classification results without destroying the seed or requiring lengthy laboratory procedures
Solution Approach 2:
The patent performs spectral analysis on intact seeds before any destructive testing would be required. This preliminary non-destructive analysis provides sufficient information for accurate classification, eliminating the need for subsequent destructive testing and significantly reducing overall testing time
3Ease of operation
If traditional sorting methods are used, then the system is simple to operate, but seed lot purity cannot be effectively improved
Solution Approach 1:
The patent introduces spectral analysis as an intermediary between simple visual inspection and complex destructive testing. This intermediary layer provides automated, objective classification based on spectral signatures, improving seed lot purity through more reliable differentiation of stress-resistant seeds while maintaining ease of operation through automated processing
Data Source
AI summary
A system for sorting seeds based on their resistance to a stress is disclosed. Batches of purified seeds sorted using the system are also disclosed.