In EDS ansehen

Rapid detection of imperfect maize kernels based on spectral and image features fusion.

Gespeichert in:
Bibliographische Detailangaben
Titel: Rapid detection of imperfect maize kernels based on spectral and image features fusion.
Autoren: Song, Kai, Zhang, Yan, Shi, Tianyu, Yang, Dong
Quelle: Journal of Food Measurement & Characterization; May2024, Vol. 18 Issue 5, p3277-3286, 10p
Schlagwörter: IMAGE fusion, SPECTRAL imaging, PARTIAL least squares regression, FEATURE extraction, MACHINE learning
Abstract: In order to quickly and non-destructively detect imperfect maize kernels, and to effectively enhance the efficiency of maize quality detection during collection and storage, identification models for imperfect maize kernels were constructed by using hyperspectral imaging (HSI) combined with machine learning techniques. Hyperspectral images of maize kernels in the range of 380–1000 nm were collected, and 10 spectral characteristic wavelengths (variables) were selected by using variable combination cluster analysis (VCPA). For grayscale images corresponding to these characteristic wavelengths, 3 texture features were extracted by using Tamura algorithm. Additionally, 3 color features and 4 morphological features were extracted through color moment analysis and regional geometry calculation, respectively. Based on the data of spectral features, image features and fusion features (spectral and image features), partial least squares regression (PLSR) and extreme learning machine (ELM) algorithms were respectively used to establish identification models for imperfect maize kernels. The results demonstrated that the overall average recognition accuracy of the ELM models was 91.60%, slightly surpassing the 91.29% achieved by the PLSR models. Notably, the ELM model based on the fusion features exhibited the highest recognition accuracy for heat-damaged kernels, achieving an accuracy rate of 97.22% in the test set. Therefore, the classification models established in this study proved to be feasible for the rapid and accurate identification of imperfect maize kernels. This can provide valuable technical support for the research and the development of non-destructive, rapid inspection equipment for imperfect kernels, as well as online batch detection. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Food Measurement & Characterization is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Datenbank: Complementary Index
Beschreibung
Abstract:In order to quickly and non-destructively detect imperfect maize kernels, and to effectively enhance the efficiency of maize quality detection during collection and storage, identification models for imperfect maize kernels were constructed by using hyperspectral imaging (HSI) combined with machine learning techniques. Hyperspectral images of maize kernels in the range of 380–1000 nm were collected, and 10 spectral characteristic wavelengths (variables) were selected by using variable combination cluster analysis (VCPA). For grayscale images corresponding to these characteristic wavelengths, 3 texture features were extracted by using Tamura algorithm. Additionally, 3 color features and 4 morphological features were extracted through color moment analysis and regional geometry calculation, respectively. Based on the data of spectral features, image features and fusion features (spectral and image features), partial least squares regression (PLSR) and extreme learning machine (ELM) algorithms were respectively used to establish identification models for imperfect maize kernels. The results demonstrated that the overall average recognition accuracy of the ELM models was 91.60%, slightly surpassing the 91.29% achieved by the PLSR models. Notably, the ELM model based on the fusion features exhibited the highest recognition accuracy for heat-damaged kernels, achieving an accuracy rate of 97.22% in the test set. Therefore, the classification models established in this study proved to be feasible for the rapid and accurate identification of imperfect maize kernels. This can provide valuable technical support for the research and the development of non-destructive, rapid inspection equipment for imperfect kernels, as well as online batch detection. [ABSTRACT FROM AUTHOR]
ISSN:21934126
DOI:10.1007/s11694-024-02402-3