Woody plant species spectrum identification method based on machine learning

Abstract

The invention relates to a woody plant species spectrum identification method based on machine learning. The method is characterized by comprising the following steps: S1, constructing a woody plant species spectrum database; S2, preprocessing the spectral data in the woody plant species spectral database to obtain a wavelet transform data set and a spectral characteristic index optimization dataset; and S3, based on the obtained continuous wavelet transform data set and the spectral characteristic index optimization data set, utilizing a machine learning algorithm to carry out woody plant species identification, and obtaining species identification precision. The invention provides a rapid and high-precision woody plant species spectrum identification method, which maintains high precision while reducing time cost, provides a new technical means for fool type species identification, and can be widely applied to the field of woody plant species spectrum classification.

Description

technical field [0001] The invention relates to a machine learning-based spectral identification method for woody plant species, which belongs to the technical field of spectral classification. Background technique [0002] Biodiversity is the foundation and source of natural ecosystem functions and services. Intense human disturbance has changed the ecosystem we depend on for survival at an unprecedented speed and scale, leading to the loss of biodiversity on a global scale. Keeping abreast of the current status, patterns, trends and threats of biodiversity can provide strong scientific and technological support for formulating biodiversity conservation policies and measures. [0003] Species identification and classification are the basis of biodiversity monitoring in terrestrial ecosystems. However, traditional species identification is mainly based on ground surveys, which is time-consuming and laborious, and the regional spatial representation and temporal continuity ...

AI Technical Summary

Problems solved by technology

However, as the number of species increases, "same object with different spectrum" and "different object with same spectrum" will increase, which will increase the difficulty of species identification, resulting in the validity of the algorithm and the credibility of the classification accuracy in its conclusions.

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