Fusarium Moniliforme Detection in Rice Seeds Using Activated Wavelengths
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Solution Overview
Problem
Current methods for detecting Fusarium moniliforme species in rice seeds are labor-intensive, require specialized knowledge, and are not suitable for large-scale, rapid, and accurate detection, while existing hyperspectral imaging techniques struggle with data dimensionality reduction and simultaneous tuning of wavelength selection and classification, failing to differentiate between different pathogenic bacteria species.
Innovation Solution
A method and system using a deep convolutional neural network (DCNN) based on activated wavelengths, involving preprocessing of hyperspectral images and a spectral gradient weighting method to identify Fusarium moniliforme species, utilizing a trained DCNN to determine activated wavelengths for accurate classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional detection methods (visual inspection, washing inspection, staining test, agar plate method) are used, then detection accuracy can be maintained with expert knowledge, but detection speed and productivity are severely limited and require specialized plant protection knowledge
Solution Approach 1:
The patent replaces manual mechanical detection methods with an automated hyperspectral imaging system combined with deep learning algorithms. The system automatically captures hyperspectral images, extracts spectral features, and classifies Fusarium species without requiring manual sampling or expert interpretation, thereby dramatically improving detection speed while eliminating the need for specialized plant protection knowledge
Solution Approach 2:
The system enables self-service detection by automating the entire detection process from image acquisition to classification. The deep learning model automatically learns optimal spectral features and classification rules from training data, allowing the system to operate independently without requiring expert operators to perform manual analysis or interpret results
2Productivity
If hyperspectral imaging technology is applied for batch detection, then productivity and detection speed are improved, but data dimensionality reduction complexity increases and differentiation between different pathogenic bacteria species becomes difficult
Solution Approach 1:
The patent transforms the high-dimensional hyperspectral data into a more manageable feature space by extracting key spectral parameters and using dimensionality reduction techniques. The system identifies and focuses on specific wavelength regions that are most discriminatory for different Fusarium species, converting complex spectral data into meaningful features that simplify subsequent classification while maintaining species differentiation capability
Solution Approach 2:
The patent addresses dimensionality issues by transitioning from analyzing all spectral bands to identifying and utilizing specific activated wavelengths that provide the most discriminatory power. This dimensional transformation reduces data complexity while preserving the essential information needed to differentiate between Fusarium species through the deep learning model's feature extraction capabilities
3Measurement precision
If wavelength selection algorithm and classification decision maker are tuned separately, then individual components can be optimized, but simultaneous global optimization cannot be achieved affecting overall model performance
Solution Approach 1:
The patent merges the wavelength selection and classification tasks into a unified deep learning framework. The model simultaneously performs feature extraction from spectral data and classification of Fusarium species in an integrated architecture, allowing both functions to be optimized together through end-to-end training rather than as separate sequential steps
Solution Approach 2:
The patent implements preliminary action by pre-training the deep learning model on a large dataset to automatically learn optimal spectral feature representations and classification boundaries. This preliminary training phase enables the model to identify activated wavelengths and classification rules simultaneously, achieving global optimization before deployment without requiring manual tuning of individual components
Data Source
AI summary
A method and system for detecting Fusarium moniliforme species of rice seeds are provided, relating to the field of rapid quality detection of rice seeds. The method includes: inputting a hyperspectral image of to-be-tested rice seeds to a model for detecting Fusarium moniliforme species of rice seed, to determine a test result of the rice seeds, where the test result is no Fusarium moniliforme or a Fusarium species. The model for detecting Fusarium moniliforme species of rice seed is determined based on activated wavelengths and an original deep convolutional neural network; the activated wavelengths are wavelengths activated by a trained deep convolutional neural network upon correct classification; and the trained deep convolutional neural network is a neural network obtained by training the original deep convolutional neural network based on the training set.