Fruit tree variety identification method based on visible near infrared spectrum

Abstract

The invention discloses a fruit tree variety identification method based on a visible near infrared spectrum. The invention relates to the fruit tree variety identification method based on the fruit tree leaf visible near infrared spectrum, which is mainly composed of a convolution noise reduction auto-encoder (CDAE) and a random forest (RF), wherein the convolution noise reduction auto-encoder is mainly used for carrying out feature extraction on fruit tree leaf visible near infrared spectrum data; and the random forest classifier is responsible for classifying the features extracted by the convolution noise reduction auto-encoder so as to identify different varieties of fruit trees. According to the fruit tree variety identification method, feature values are extracted by using the convolution noise reduction auto-encoder, the method has the advantages of high classification accuracy, strong noise immunity, good feature extraction capability, omission of a data preprocessing step and no need of spectrum preprocessing, the leaf spectrum is analyzed by using the method, the performance of a random forest algorithm is improved by using the convolution noise reduction auto-encoder, and compared with a traditional random forest algorithm susceptible to noise interference, the method has great progress in the aspect of robustness; and a novel rapid identification method is provided for apple tree variety identification.

Description

technical field [0001] The invention relates to a method for identifying fruit tree varieties based on visible and near-infrared spectra, and belongs to the technical field of fruit tree variety identification. Background technique [0002] Fruit tree planting is an important industrial economy in my country. Many different types of fruit trees are planted in China. Different fruit tree varieties have different economic values. Traditional identification of fruit tree species often relies on the experience of fruit farmers, mainly based on the comprehensive characteristics of the plants. The morphological identification method is intuitive and easy to operate, but the phylogenetic relationship and plant morphological classification are inconsistent in some cases, and relying on personal experience has great uncertainty, which is not suitable for large-scale measurement of fruit tree varieties. The biochemical identification method can identify varieties with different geneti...

AI Technical Summary

Problems solved by technology

[0004] The technical principle of the present invention is to design a kind of fruit tree species identification algorithm based on visible and near-infrared spectrum: firstly, use unmanned aerial vehicle carrier spectrometer or remote sensing satellite and other instruments and equipment to obtain the spectral data of ground objects in a certain area, and then use convolution to reduce noise The self-encoder (CDAE) extracts the features of the spectral data, and then imports the feature values ​​into the random forest (RF) classifier to realize the classification of fruit tree varieties. Extracted and condensed data is more conducive to modeling

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