Search

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. [ABSTRACT FROM AUTHOR]

Detritus geochemical information has been proven through research to be an effective prospecting method in mineral exploration. However, the traditional detritus metal content monitoring methods based on field sampling and laboratory chemical analysis are time-consuming and may not meet the requirements of large-scale metal content monitoring. In this study, we obtained 95 detritus samples and seven HySpex hyperspectral imagery scenes with a spatial resolution of 1 m from Karatag Gobi area, Xinjiang, China, and used partial least squares and wavebands selection methods to explore the usefulness of super-low-altitude HySpex hyperspectral images in estimating detritus feasibility and effectiveness of Cu element content. The results show that: (1) among all the inversion models of transformed spectra, power-logarithm transformation spectrum was the optimal prediction model (coefficient of determination(R2) = 0.586, mean absolute error(MAE) = 21.405); (2) compared to the genetic algorithm (GA) and continuous projection algorithm (SPA), the competitive weighted resampling algorithm (CARS) was the optimal feature band-screening method. The R2 of the inversion model was constructed based on the characteristic bands selected by CARS reaching 0.734, which was higher than that of GA (0.519) and SPA (0.691), and the MAE (19.926) was the lowest. Only 20 bands were used in the model construction, which is lower than that of GA (105) and SPA (42); (3) The power-logarithm transforms, and CARS combined with the model of HySpex hyperspectral images and the Cu content distribution in the study area were obtained, consistent with the actual survey results on the ground. Our results prove that the method incorporating the HySpex hyperspectral data to invert copper content in detritus is feasible and effective, and provides data and a reference method for obtaining geochemical element distribution in a large area and for reducing key areas of geological exploration in the future. [ABSTRACT FROM AUTHOR]