Version 1
: Received: 15 March 2024 / Approved: 15 March 2024 / Online: 15 March 2024 (08:56:21 CET)
How to cite:
Li, C.; Tan, Y.; Liu, C.; Gao, X.; Lv, Z.; Guo, W. Spectral Fusion Based on Hyperspectral Imaging Technology for Discrimination of Rice Varieties. Preprints2024, 2024030886. https://doi.org/10.20944/preprints202403.0886.v1
Li, C.; Tan, Y.; Liu, C.; Gao, X.; Lv, Z.; Guo, W. Spectral Fusion Based on Hyperspectral Imaging Technology for Discrimination of Rice Varieties. Preprints 2024, 2024030886. https://doi.org/10.20944/preprints202403.0886.v1
Li, C.; Tan, Y.; Liu, C.; Gao, X.; Lv, Z.; Guo, W. Spectral Fusion Based on Hyperspectral Imaging Technology for Discrimination of Rice Varieties. Preprints2024, 2024030886. https://doi.org/10.20944/preprints202403.0886.v1
APA Style
Li, C., Tan, Y., Liu, C., Gao, X., Lv, Z., & Guo, W. (2024). Spectral Fusion Based on Hyperspectral Imaging Technology for Discrimination of Rice Varieties. Preprints. https://doi.org/10.20944/preprints202403.0886.v1
Chicago/Turabian Style
Li, C., Zhong Lv and Wenjing Guo. 2024 "Spectral Fusion Based on Hyperspectral Imaging Technology for Discrimination of Rice Varieties" Preprints. https://doi.org/10.20944/preprints202403.0886.v1
Abstract
To ensure consumers can purchase high-quality rice, accurate identification of rice varieties is particularly important. Methods: This article conducts research based on machine learning algorithms from the perspectives of image, spectrum, and spectrogram fusion. Six types of hyperspectral image data of rice were preprocessed using convolutional smoothing (SG) and multiple scatter correction (MSC). Texture information of the images was extracted using gray-level co-occurrence matrix. Spectra, texture, and spectrogram data were fused into a new matrix. Spectral, texture, and spectrogram fusion data were used as inputs for the model, and support vector machines (SVM), logistic regression (LR), and K-nearest neighbors (KNN) classification models were constructed and compared. Results: From the classification results, the spectrogram fusion classification performance was better than classification models using only spectra or texture. Conclusion: The research results showed that the accuracy of SVM and LR classification models exceeded 90%, and the LR model performed the best, effectively classifying rice varieties.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
