A method for detecting chlorophyll content in plant leaves

The spectral index is calculated by combining the machine learning model to predict the chlorophyll content, which solves the problems of low detection accuracy and poor applicability in the existing technology, and achieves high-precision and wide-ranging detection of chlorophyll content.

CN119310018BActive Publication Date: 2025-03-28ZHEJIANG UNIV OF TECH
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Patent Information

Application Number
CN202411859215.9
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-12-17
Publication Date
2025-03-28
Estimated Expiration
2044-12-17

AI Technical Summary

Technical Problem

The existing chlorophyll content detection methods based on spectral analysis technology have problems such as low detection accuracy, poor applicability, susceptibility to noise, and failure to fully consider the complex nonlinear relationship between chlorophyll and spectral reflectivity.

Method used

The spectral index was calculated using hyperspectral reflectivity data, and the chlorophyll content was predicted through machine learning models. The method includes collecting hyperspectral reflectivity data from plant leaves, calculating the spectral index, and inputting it into the chlorophyll content prediction model. At the same time, adaptive segmentation index and weight are introduced, and the calculation of spectral index is dynamically adjusted to enhance the adaptability and accuracy of detection.

Benefits of technology

Non-destructive testing of plant leaves is achieved, detection accuracy is improved, and it is suitable for different plant leaves in different growth environments, with strong applicability and strong noise resistance.

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Abstract

The present invention discloses a method for detecting the chlorophyll content of plant leaves. The method comprises the following steps: collecting hyperspectral reflectance data of a plant leaf sample to be tested, wherein the hyperspectral reflectance data comprises n wavelength pairs and the spectral reflectance corresponding to each wavelength in the wavelength pair; calculating the spectral index corresponding to the plant leaf sample to be tested according to the hyperspectral reflectance data; inputting the spectral index into a chlorophyll content prediction model, and the chlorophyll content prediction model outputs the chlorophyll content of the plant leaf sample to be tested. The present invention can perform non-destructive testing on plant leaves, improves the detection accuracy, can be applied to different plant leaves in different growth environments, and has strong applicability.
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