An intelligent identification method for foreign fibers in seed cotton based on hyperspectral imaging with the PCA-AlexNet model

The integration of hyperspectral imaging and deep learning for foreign fiber detection has primarily focused on plastic film. However, detecting various foreign fibers in long-staple cotton, particularly those that are white, transparent or similar in color, remains a significant challenge. The spectral response differences of various foreign fibers across different wavelengths are significant, which makes hyperspectral multi-target detection more complex. To address this challenge, a hyperspectral identification algorithm is proposed. First, hyperspectral image of the experimental samples are captured, and principal component analysis (PCA) is applied to select the optimal feature bands for recognition by a convolutional neural network. Next, the AlexNet model is fine-tuned to optimal parameters using the primary feature bands. After multidimensional experimental validation, the PCA-AlexNet model efficiently identifies foreign fibers. Finally, after analyzing the experimental results from multiple perspectives, the fiber identification model is identified as PCA-AlexNet-23. The results show that the PCA-AlexNet-23 model excels in identifying multiple fibers, achieving an overall accuracy of 97.2%, an average accuracy of 95.2%, and a Kappa coefficient of 93.1%. These accuracy rates outperform those of a support vector machine, artificial neural network, LDA-VGGNet and LDA-LeNet models. In the experimental tests, the overall foreign fiber removal rate exceeds 85%.