A Soil Profile Type Recognition Method Based on KNN Classification

Abstract

The invention discloses a soil profile type identification method based on KNN classification, which comprises the following steps: S1, extracting the trough characteristics of a spectral curve; S2, extracting the peak characteristics of a spectral curve; S3, training Collect and test set data, extract the trough characteristics of the soil spectral curve, and store them in the data set c1; S4, for the training set and test set data, after differential derivation processing, extract the peak and trough features of the first derivative curve of the soil spectrum, and store them in the data Set c2; S5, merge the data sets c1 and c2 to obtain the attribute data set L, after normalizing the attribute data set L, based on the KNN classification method, carry out the soil type identification of the soil spectral curve of the test set; S6, based on the maximum According to the proportion principle, the soil type of the soil profile of the test set is determined. This method has greater advantages in ultra-high-dimensional classification, and can be used in the case of high-dimensional sample data and many soil types.

Description

technical field [0001] The invention belongs to the field of soil remote sensing, and in particular relates to a method for identifying soil types based on K-Nearest Neighbor (KNN for short). Background technique [0002] Soil classification is based on the comprehensive differences in soil physical properties, classifying soil types at different levels, so as to promote agricultural technology and soil improvement technology according to local conditions. The identification of soil types needs to obtain a large amount of diagnostic information such as soil morphology, physics, chemistry and even biology. Some of the information (such as morphology, bulk density, etc.) can be obtained by visual observation or simple measurement, but most of the physical and chemical information is in the traditional It can only be obtained after laboratory testing and analysis. Therefore, the identification and identification of soil types is usually costly and requires the involvement of s...

AI Technical Summary

Problems solved by technology

However, when faced with many soil types and a large number of soil samples, the learning time of this method is too long, and may even fail to achieve the purpose of learning, so when faced with many soil types and a large number of soil sample data, the overall classification effect is not good. not ideal

In addition, only 0-20cm plow layer soil samples are used in related methods, and the soil spectral information at different depths in the same section cannot be fully utilized.

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