Spectral Index Screening for Rice Bacterial Blight Resistance
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Solution Overview
Problem
Current methods for screening rice varieties resistant to bacterial blight are labor-intensive and inefficient, requiring manual measurement of leaf lesions, which hinders the breeding process and is costly, while existing spectral analysis methods suffer from redundancy in spectral band usage, limiting rapid and low-cost applications.
Innovation Solution
A method and system utilizing a self-attention model to process spectral data from rice leaves, applying a threshold segmentation algorithm to extract average spectral information and lesion proportions, training a deep learning algorithm to construct a regression model, determining an optimal band combination and weight values to quantify lesion areas, and identifying spectral indexes for rapid identification of resistant varieties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual measurement of leaf lesions is used to screen rice varieties resistant to bacterial blight, then measurement precision is improved, but productivity deteriorates due to huge labor costs and low efficiency
Solution Approach 1:
The patent replaces the manual mechanical measurement system with an automated image processing system. The system captures images of rice leaves using a camera and automatically processes them through algorithms to measure lesion area and calculate resistance indices, eliminating the need for manual measurement while maintaining precision and significantly improving productivity through automation
Solution Approach 2:
The patent creates a digital copy (image) of the physical rice leaf and its lesions. Instead of directly measuring the physical leaf, the system works with a digital representation, allowing for rapid, automated analysis without physical contact or manual intervention, thus resolving the contradiction between precision and productivity
2Measurement precision
If multi-source spectral data fusion is used for early detection of rice leaf disease, then measurement precision is improved, but device complexity worsens due to redundancy in spectral bands limiting rapid and low-cost applications
Solution Approach 1:
The patent extracts only the essential spectral bands needed for disease detection from the full spectral range. Instead of using all available spectral data, the system identifies and processes only the most informative bands, reducing data redundancy and computational complexity while maintaining detection precision
Solution Approach 2:
The patent transforms the spectral data by converting raw spectral reflectance values into vegetation indices and disease severity indices. This parameter transformation simplifies the data structure, reduces dimensionality, and makes the data more suitable for rapid analysis while preserving the essential diagnostic information
Data Source
AI summary
A method and system for screening spectral indexes of rice resistant to bacteria. The method includes: processing spectral data of a test sample by a threshold segmentation algorithm to obtain average spectral information of each spectral image and a proportion of lesions corresponding to each spectral image; training a deep learning algorithm model based on a self-attention mechanism by using the average spectral information of each spectral image and the proportion of the corresponding lesions to construct a regression model for evaluating an area of the lesions; determining an optimal band combination and a weight value corresponding to each band in the optimal band combination based on the regression model for evaluating the area of the lesions, and then determining the spectral indexes; and identifying differences between rice of different genotypes at different times of infection by using the spectral indexes, and screening rice varieties resistant to bacterial blight.