Construction and recognition method of bp neural network for different brown rice grain recognition

Abstract

The invention discloses a BP neural network construction and identification method for identifying different brown rice grains, adopting the following technical scheme: including the following steps: 1) acquiring images: 2) image preprocessing: 3) extracting image feature information of different brown rice grains: 4) main Component analysis reduces dimensionality of image feature information; 5) Design BP neural network structure: 6) Train neural network, and use the BP network neural network constructed by any of the above-mentioned construction methods to identify different brown rice grains. The method proposed by the present invention obtains images of different types of brown rice grains through black as the background, uses image processing technology to obtain its characteristic information, and uses principal component analysis to reduce the dimensionality of the characteristic information, and finally uses BP neural network to perform different types of brown rice grains. identify. The method can identify different types of grains objectively, accurately and quickly, and overcomes the disadvantages of traditional manual detection.

Description

technical field [0001] The invention relates to a method for detecting brown rice grain types based on computer image processing technology combined with BP neural network. It belongs to the field of computer automatic identification. Background technique [0002] Brown rice is used as storage raw material and rice milling raw material, its quality directly affects the edible and nutritional value of rice, and then affects its economic value and food security. Imperfect kernels, uncooked kernels, and dead rice are unavoidable components in brown rice. They not only affect the processing quality of subsequent rice, but also affect the cooking quality of rice. The type and content of brown rice grains are greatly affected by rice varieties and growth environments. At present, the detection of brown rice grain types in my country is mainly through manual visual inspection. This method is highly subjective, labor-intensive, and has large errors, which is not conducive to rapid...

AI Technical Summary

Problems solved by technology

[0003] In order to solve the detection problem of quickly and accurately identifying different types of brown rice grains, the present invention proposes a method based on computer image processing technology and utilizes BP neural network to identify different types of brown rice grains.