Electrostatic separation motion analysis of machine-harvested cotton and residual film based on CFD

Zhang, Hongzhou; Wang, Qiaohua; Li, Yong; Liu, Yuanjie; Jia, Danping

doi:10.3233/jcm-194073

Cited by 2 publications

(4 citation statements)

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“…where λ j denotes eigenvalues of the formula (6); u j is the eigenvector of formula (7). The eigenvalue v j of formula ( 7) can form the eigenspace:…”

Section: Dimensionality Reduction and Preprocessing Of Hyperspectral ...mentioning

confidence: 99%

“…Therefore, it is important to identify the film on cotton to increase the economic value of cotton. 5 The traditional techniques for removing film can be divided into three types, including mechanic sorting, 6 electromagnetic separation, 7 and photoelectric sorting. 8 Mechanic sorting is a relatively early and traditional agricultural sorting method, and roll winding and gas-solid separation are the most widely used technologies.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al established a solid model of electrostatic separation between mechanical cotton harvesting to remove the particles effectively. 7 With the application of image sensors to the field of agricultural sorting, photoelectric sorting technology has been widely promoted. Zhao et al built an efficient recognition system based on a light-emitting diode (LED) light source and charge-coupled device (CCD) camera to accurately recognize foreign fibers mixed in cotton.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent identification of film on cotton based on hyperspectral imaging and convolutional neural network

et al. 2022

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The identification of the film on cotton is of great significance for the improvement of cotton quality. Most of the existing technologies are dedicated to removing colored foreign fibers from cotton using photoelectric sorting methods. However, the current technologies are difficult to identify colorless transparent film, which becomes an obstacle for the harvest of high-quality cotton. In this paper, an intelligent identification method is proposed to identify the colorless and transparent film on cotton, based on short-wave near-infrared hyperspectral imaging and convolutional neural network (CNN). The algorithm includes black-and-white correction of hyperspectral images, hyperspectral data dimensionality reduction, CNN model training and testing. The key technology is that the features of the hyperspectral image data are degraded by the principal component analysis (PCA) to reduce the amount of computing time. The main innovation is that the colorless and transparent film on cotton can be accurately identified through a CNN with the performance of automatic feature extraction. The experimental results show that the proposed method can greatly improve the identification precision, compared with the traditional methods. After the simulation experiment, the method proposed in this paper has a recognition rate of 98.5% for film. After field testing, the selection rate of film is as high as 96.5%, which meets the actual production needs.

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“…where λ j denotes eigenvalues of the formula (6); u j is the eigenvector of formula (7). The eigenvalue v j of formula ( 7) can form the eigenspace:…”

Section: Dimensionality Reduction and Preprocessing Of Hyperspectral ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent identification of film on cotton based on hyperspectral imaging and convolutional neural network

et al. 2022

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“…[2]. Zhang et al used computational fluid dynamics (CFD) to model the electrostatic separation of mechanical cotton harvesting and residual plastic film by flying the experimental sample into an electric field at different speeds and applying different electric field forces [3]. With the increasing prevalence of machine vision, optical color separation has become a popular method for the intelligent classification of agricultural products.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Sorting Method of Film in Cotton Combining Hyperspectral Imaging and the AlexNet-PCA Algorithm

Zhao

Xin

et al. 2023

Sensors

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Long-staple cotton from Xinjiang is renowned for its exceptional quality. However, it is susceptible to contamination with plastic film during mechanical picking. To address the issue of tricky removal of film in seed cotton, a technique based on hyperspectral images and AlexNet-PCA is proposed to identify the colorless and transparent film of the seed cotton. The method consists of black and white correction of hyperspectral images, dimensionality reduction of hyperspectral data, and training and testing of convolutional neural network (CNN) models. The key technique is to find the optimal way to reduce the dimensionality of the hyperspectral data, thus reducing the computational cost. The biggest innovation of the paper is the combination of CNNs and dimensionality reduction methods to achieve high-precision intelligent recognition of transparent plastic films. Experiments with three dimensionality reduction methods and three CNN architectures are conducted to seek the optimal model for plastic film recognition. The results demonstrate that AlexNet-PCA-12 achieves the highest recognition accuracy and cost performance in dimensionality reduction. In the practical application sorting tests, the method proposed in this paper achieved a 97.02% removal rate of plastic film, which provides a modern theoretical model and effective method for high-precision identification of heteropolymers in seed cotton.

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Electrostatic separation motion analysis of machine-harvested cotton and residual film based on CFD

Cited by 2 publications

References 4 publications

Intelligent identification of film on cotton based on hyperspectral imaging and convolutional neural network

Intelligent identification of film on cotton based on hyperspectral imaging and convolutional neural network

An Intelligent Sorting Method of Film in Cotton Combining Hyperspectral Imaging and the AlexNet-PCA Algorithm

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