Cordyceps sinensis detection method based on self-encoding feature learning

A technology of Cordyceps sinensis and feature learning, applied in the fields of instrument, calculation, character and pattern recognition, etc., can solve the problem that there is no detection method for Cordyceps sinensis, and it has been seen in the literature.

Active Publication Date: 2017-05-31
NANJING UNIV OF SCI & TECH
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  • Cordyceps sinensis detection method based on self-encoding feature learning
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  • Cordyceps sinensis detection method based on self-encoding feature learning

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Embodiment Construction

[0011] The present invention is based on the Cordyceps sinensis detection method of self-encoding feature learning, and its steps are:

[0012] 1. Preparation of samples

[0013] 1) collecting a series of images containing Cordyceps sinensis;

[0014] 2) Extract the sub-blocks containing Cordyceps sinensis and the sub-blocks containing the background, the extraction ratio is 1:10, and scale them to the same size;

[0015] 3) Classify the samples according to the positive and negative sample categories and number them.

[0016] 2. Classification model training

[0017] 1) Shuffle the order of the sample library, use a two-layer stacked self-encoder network for training, and obtain the model parameters after training;

[0018] 2) Encode the sample library using the trained model,

[0019] 3) At the same time, the code and the sample category label are brought into the nonlinear kernel support vector machine for training to obtain the classification model parameters, wherein ...

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Abstract

The invention discloses a cordyceps sinensis detection method based on self-encoding feature learning. The cordyceps sinensis detection method comprises the following steps of (1) collecting a series of images containing cordyceps sinensis; (2) extracting cordyceps sinensis and other backgrounds from the image, and making positive and negative samples with same size; (3) training a self-encoding model by the extracted sample images, and obtaining self-encoding model parameters; (4) encoding the samples through the self-encoding model; (5) classifying and training the obtained encodes and sample types by a nonlinear support vector machine, and obtaining classifying model parameters; (6) collecting the to-be-detected cordyceps sinensis images, and blocking under multiple scales; (7) encoding each image by the self-encoding model, and using the nonlinear support vector machine to classify and record positions; (8) removing the detected coinciding areas, and identifying all non-coinciding areas onto the to-be-detected images. The method can be used for automatically detecting the cordyceps sinensis in the environment under the complicated background.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence application, in particular to a method for detecting Cordyceps sinensis based on self-encoding feature learning. Background technique [0002] Cordyceps sinensis, also known as Cordyceps sinensis, is a precious nourishing medicinal material commonly used by Chinese folks. Its nutritional content is higher than that of ginseng. It can be used as medicine or edible. It is an excellent delicacy with high nutritional value. Cordyceps sinensis is mainly produced in the upper reaches of the Jinsha River, Lancang River, and Nujiang River. It reaches Liangshan in Sichuan Province in the east, Pulan County in Tibet in the west, Minshan in Gansu Province in the north, and the Himalayas and Yulong Snow Mountain in Yunnan Province in the south. The best Cordyceps grows on sunny and humid hillsides, meadows, and bushes with soft and fertile soil at an altitude of about 3,000 to 5,500 meters, a...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2411G06F18/214
Inventor 张浩峰周玲莉刘世钰
Owner NANJING UNIV OF SCI & TECH
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