Nondestructive detection and classification of impurities-containing seed cotton based on hyperspectral imaging and one-dimensional convolutional neural network^☆

Abstract

The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles, and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textiles. By fusing band combination optimization with deep learning, this study aims to achieve more efficient and accurate detection of film impurities in seed cotton on the production line. By applying hyperspectral imaging and a one-dimensional deep learning algorithm, we detect and classify impurities in seed cotton after harvest. The main categories detected include pure cotton, conveyor belt, film covering seed cotton, and film adhered to the conveyor belt. The proposed method achieves an impurity detection rate of 99.698%. To further ensure the feasibility and practical application potential of this strategy, we compare our results against existing mainstream methods. In addition, the model shows excellent recognition performance on pseudo-color images of real samples. With a processing time of 11.764 μs per pixel from experimental data, it shows a much improved speed requirement while maintaining the accuracy of real production lines. This strategy provides an accurate and efficient method for removing impurities during cotton processing.

Keywords

Seed cotton / Film impurity / Hyperspectral imaging / Band optimization / Classification

CRediT authorship contribution statement

Yeqi Fei: Writing - review & editing, Writing - original draft, Software, Methodology, Formal analysis, Conceptualization. Zhenye Li: Writing - original draft, Validation, Software, Formal analysis, Data curation. Tingting Zhu: Writing - original draft, Validation, Data curation. Zengtao Chen: Writing - review & editing, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition. Chao Ni: Writing - review & editing, Writing - original draft, Supervision, Project administration, Methodology, Funding acquisition, Data curation.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work is supported in part by the Six Talent Peaks Project in Jiangsu Province under Grant 013040315, in part by the China Textile Industry Federation Science and Technology Guidance Project under Grant 2017107, in part by the National Natural Science Foundation of China under Grant 31570714, and in part by the China Scholarship Council under Grant 202108320290.

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