Rice growth state monitoring method based on hyperspectral imaging

By combining hyperspectral imaging with continuous wavelet transform and independent component analysis constrained by bio-fingerprint, the problem of distinguishing between disease and nutrient deficiency symptoms in rice growth status monitoring has been solved, enabling accurate assessment and management of rice growth status.

CN121963103AActive Publication Date: 2026-05-01RICE RES INST GUANGDONG ACADEMY OF AGRI SCI
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Patent Information

Application Number
CN202610432582.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-03
Publication Date
2026-05-01
Estimated Expiration
2046-04-03

AI Technical Summary

Technical Problem

Existing technologies cannot accurately distinguish between early diseases and nutrient deficiency symptoms during rice growth, and weak disease signals are easily drowned out by background noise, leading to misdiagnosis and missed detection in monitoring results, making it difficult to meet the needs of precision management.

Method used

By employing a hyperspectral imaging-based method, combined with continuous wavelet transform and independent component analysis constrained by bio-fingerprint, the precise unmixing of spectral characteristics and growth status assessment of rice canopy were achieved through scale-specific response intensity and bio-fingerprint consistency factor.

Benefits of technology

It significantly improved the detection accuracy of early growth stress, provided a scientific quantitative monitoring basis, ensured the accuracy and reliability of monitoring results, and supported precise field management decisions.

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Abstract

The invention belongs to the technical field of image processing, and particularly relates to a rice growth state monitoring method based on hyperspectral imaging, comprising the following steps: acquiring a hyperspectral reflectivity curve of a rice canopy, the hyperspectral reflectivity curve reflecting spectral response characteristics of rice under different wavebands; mapping the hyperspectral reflectivity curve to a time-frequency space by using continuous wavelet transform to obtain a wavelet coefficient matrix, determining an optimal feature scale from the wavelet coefficient matrix according to scale specific response intensities under different scales, and extracting a feature vector; and performing independent component analysis and unmixing processing on the feature vector based on the biological fingerprint constraint, and decomposing the feature vector into a plurality of independent components. According to the method, signal separation is guided through biological priori knowledge, the misdiagnosis problem caused by the same spectrum of foreign matters is effectively solved, the detection accuracy of early weak diseases is remarkably improved, and a quantitative basis with agronomic significance is provided for precise management of rice.
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