Seed cotton residual film sorting device and algorithm based on hyperspectral imaging and deep learning

Abstract

The invention discloses a seed cotton residual film sorting device and algorithm based on hyperspectral imaging and deep learning; the residual film sorting device comprises a cotton distribution box,a high-spectral imager, a dome halogen lamp, an industrial control computerr, a black rubber crawler belt, a high-speed spraying valve, an air suction opening and a fan. The seed cotton to be treatedenters the cotton distribution box from the cotton inlet to be opened, then the hyper-spectral imager is used for acquiring a reflection spectrum image, and then image processing is carried out through the industrial control computer; after the processing is finished and a certain time is delayed, a driving box drives the high-speed spraying valve to spray the residual film at the corresponding position or the seed cotton containing the residual film to the air suction opening, the fan suck the residual film and the seed cotton, and the residual film located at the bottom of the seed cotton can be sucked into the air suction opening due to the air pressure generated by the fan; and the other seed cotton can fall at the designated position due to inertia, so that the sorting is completed.According to the device and algorithm, rapid and accurate recognition and classification of the seed cotton residual membranes can be realized, and the technical problem of residual film sorting for many years in the industry is solved.

Description

technical field [0001] The invention relates to the field of identification and sorting of foreign fibers of seed cotton, in particular to a sorting device and algorithm for residual film of seed cotton based on hyperspectral imaging and deep learning. Background technique [0002] my country is a big cotton production and consumption country, and Xinjiang is the largest and most important cotton planting base in my country. However, Xinjiang adopts large-scale mechanized planting of cotton, and plastic film is inevitably mixed in during the mechanized picking process. The remaining plastic film is not only large in quantity, but also in shape Varies, the color is not too different from cotton, so it is difficult to clean up. At present, many detection algorithms have been used to detect impurities such as cotton bolls, cotton husks, nylon ropes, and cloth strips in cotton. These impurities can be identified by color cameras and black-and-white cameras, but color cameras and ...

AI Technical Summary

Problems solved by technology

[0002] my country is a big cotton production and consumption country, and Xinjiang is the largest and most important cotton planting base in my country. However, Xinjiang adopts large-scale mechanized cotton planting, and plastic film is inevitably mixed in during the mechanized picking process. The residual plastic film is not only large in quantity, but also in shape Varies, the color is not too different from cotton, so it is difficult to clean

At present, many detection algorithms have been used to detect impurities such as cotton bolls, cotton husks, nylon ropes, and cloth strips in cotton. These impurities can be identified by color cameras and black-and-white cameras, but color cameras and black-and-white cameras cannot identify residual impurities such as transparent plastic film.

Incomplete cleaning of residual film will seriously affect the quality of textile products

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