Seed Sorting by Neural Network for Stress Resistance Classification
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Solution Overview
Problem
Existing methods for sorting seeds based on stress resistance are inefficient and often require destructive testing, failing to accurately differentiate between visually and physically similar seeds.
Innovation Solution
A system utilizing a neural network that classifies seeds as stress-resistant or non-stress-resistant based on visual and physical properties, trained on a dataset of seed images, enabling automated sorting without destructive testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sorting methods are used, then seeds can be sorted by visual inspection, but the accuracy is insufficient to differentiate between visually similar seeds with different stress resistance
Solution Approach 1:
The patent replaces traditional mechanical/optical sorting systems with a neural network-based classification system. The neural network processes seed images and predicts stress resistance characteristics, achieving high classification accuracy without relying on complex mechanical sorting mechanisms. This substitution of mechanical systems with intelligent algorithms resolves the contradiction between measurement precision and device complexity.
Solution Approach 2:
The patent transforms the classification problem from visual feature analysis to multi-parameter prediction by the neural network. Instead of relying on single visual characteristics, the system analyzes multiple parameters simultaneously (visual features, physical properties, stress resistance indicators) to achieve accurate classification. This parameter transformation enables high precision classification while maintaining system simplicity.
2Measurement precision
If destructive testing is used to determine stress resistance, then accurate classification can be achieved, but the sorting process becomes inefficient and time-consuming
Solution Approach 1:
The patent performs preliminary classification of seeds based on their visual and physical characteristics using a neural network, before any destructive testing is needed. By predicting stress resistance from non-destructive image analysis, the system identifies which seeds require further testing and which can be directly sorted. This preliminary action significantly improves sorting efficiency while maintaining classification accuracy.
Solution Approach 2:
The patent creates digital copies of seeds through image capture and processing, allowing the neural network to analyze and classify seeds without physically handling or destroying them. This digital copying approach enables accurate classification while preserving the physical seeds for sorting, thereby maintaining both high measurement precision and productivity.
3Measurement precision
If manual visual inspection is used, then the system remains simple, but it cannot accurately differentiate between seeds with similar visual and physical properties
Solution Approach 1:
The patent replaces manual visual inspection with an automated neural network-based image analysis system. The neural network automatically extracts features from seed images and predicts stress resistance characteristics, achieving differentiation accuracy that exceeds human capability. This automation substitution resolves the contradiction by providing both high precision differentiation and automated operation.
Solution Approach 2:
The patent transforms the inspection process from human visual assessment to multi-parameter computational analysis. The neural network evaluates numerous parameters simultaneously (color, texture, shape, structural features) that are beyond human visual capability, enabling accurate differentiation of visually similar seeds. This parameter expansion achieves high differentiation accuracy while maintaining automated operation.
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.