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.