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

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 i...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 23; číslo 16; s. 7041
Hlavní autoři: Li, Quang, Zhao, Ling, Yu, Xin, Liu, Zongbin, Zhang, Yiqing
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.08.2023
MDPI
Témata:
Accuracy
Algorithms
Cameras
Classification
convolutional neural network
Cotton
Deep learning
dimension reduction
Discriminant analysis
Electric fields
hyperspectral image
Methods
Neural networks
Remote sensing
seed cotton
Spectrum analysis
Support vector machines
Vision systems
China
Taiwan
ISSN:1424-8220, 1424-8220
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Shrnutí: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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ISSN:1424-8220
1424-8220
DOI:10.3390/s23167041