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Objective To achieve accurate and non-destructive detection of Zea mays L. seed maturity by applying hyperspectral imaging technology combined with multimodal fusion methods. Methods Hyperspectral images of high and low maturity Zea mays L. seeds were acquired. The cascade algorithm of bootstrapping soft shrinkage and successive projections algorithm (BOSS-SPA) was used for feature wavelength extraction, while the gray-level co-occurrence matrix method (GLCM) was used for image texture feature extraction. Five feature parameters—energy, entropy, correlation, homogeneity and contrast were selected to integrate the spectra with the image data in a feature level fusion. Results The partial least squares-discriminant analysis (PLS-DA) and least squares support vector machine (LS-SVM) were used to establish a Zea mays L. seed maturity classification model. The use of Savitzky-Golay convolution smoothing-standard normal variable transformation (SG-SNV) was identified as the best spectral preprocessing method, and the 11 wavelengths extracted using the BOSS-SPA method showed good modelling performance, and the overall recognition accuracies of the model test set based on the fused data of the spectral images all reached over 95%. Conclusion Hyperspectral technology combined with multimodal feature fusion method is expected to provide a practical reference method for non-destructive detection of Zea mays L. seed maturity.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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