Plant leaf identification method based on manifold learning

Abstract

A leaf identifying method based on manifold learning belongs to the technical field of biological feature identification. The method includes: (1) the collection and the pretreatment on the data of the leaf; (2) the characteristic extraction of the leaf; (3) the training of a sorter and the testing of the leaf data. At first, the leaf data is collected by a device; then de-noising, smoothing, dividing, normalizing, graying and vectorization treatments are carried out; then a semi-monitoring manifold learning arithmetic is used for extracting the linear characteristics from the leaf data; finally, a nearest neighboring sorter is adopted for sorting. A plurality of pre-treatments are carried out on the collected data, thus effectively restraining the effect of noise. The semi-monitoring manifold learning arithmetic can effectively detect the inside structure of the distribution of the leafa data and the monitoring information is led in for improving the separability of the data. The semi-monitoring manifold learning arithmetic is a linear characteristic extraction method which extensively reduces the calculation complexity of the arithmetic.

Description

[0001] Field [0002] The invention relates to the technical field of biological feature recognition, in particular to a plant leaf recognition method based on manifold learning. Background technique [0003] Plants are one of the most abundant and widely distributed forms of life on Earth. Plants are an important genetic resource for human survival and development, an important source of food for human beings, and an essential resource for human production and life. At the same time, plants play a vital role in soil and water conservation, desert suppression and climate improvement. In recent years, with the increase of human production activities, the ecological environment has been continuously destroyed. According to survey statistics, about 34,000 plant species in the world are on the verge of extinction, accounting for 13% of the 250,000 known plant species in the world. Conversely, widespread plant species extinctions have had serious consequences for humans and ecos...

AI Technical Summary

Problems solved by technology

In 2000, Saitoh et al. used images of flowers and leaves to identify wild flowers, but this method requires two images of flowers and leaves

Principal component analysis works well for data with a linear structure. It discovers the linear structure of the data by looking for the second-order statistical properties of the data, but it cannot find the real distribution structure for highly nonlinear data.

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