Enhancing grain drying methods with hyperspectral imaging technology: A visual analysis

This study proposes a recognition model for different drying methods of grain using hyperspectral imaging technology (HSI) and multivariate analysis. Fresh harvested grain samples were dried using three different methods: rotating ventilation drying, mechanical drying, and natural drying. Hyperspectral images of the samples were collected within the 388-1065 nm band range. The spectral features of the samples were extracted using principal component analysis (PCA), while the texture features were extracted using second-order probability statistical filtering. Partial least squares regression (PLSR) drying models with different characteristics were established. At the same time, a BPNN (Back-propagation neural network, BPNN) based on spectral texture fusion features was established to compare the recognition effects of different models. Texture analysis indicated that the mean-image had the clearest contour, and the texture characteristics of mechanical drying were smaller than those of rotating ventilation drying and natural drying. The BPNN model established using spectral-texture feature variables showed the best performance in distinguishing grain in different drying modes, with a prediction model obtained based on the correlation coefficients of special variables. The spectral and texture feature values were fused for pseudo-color visualization expression, and the three drying methods of grain showed different colors. This study provides a reference for non-destructive and rapid detection of grain with different drying methods.

Bibliographic Details

Main Authors:	Yang, Sicheng, Cao, Yang, Li, Chuanjie, Castagnini, Juan Manuel, Barba, Francisco Jose, Shan, Changyao, Zhou, Jianjun
Other Authors:	#NODATA#
Format:	artículo biblioteca
Language:	English
Published:	Elsevier 2024-02-08
Subjects:	Grain drying, Hyperspectral imaging, Partial least squares model, Visualization,
Online Access:	http://hdl.handle.net/10261/348502 https://api.elsevier.com/content/abstract/scopus_id/85184815871
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