Full
Outline
To explore the effective extraction of internal and external morphological features of Citrullus lanatus seeds and the identification of their varieties by applying terahertz (THz) mapping technology.
A total of 120 seeds from 3 common Citrullus lanatus varieties, “Ruixin”, “Langchao 1” and “Xiangxiu”, were selected as experimental samples, and THz transmission spectral images were collected. First, a method based on Sym6 wavelet transform was used for spectral denoising and image correction. Subsequently, correlation coefficient imaging was employed to reconstruct THz pseudo-color images of the seed coat and kernel, and the DeepLab V3+semantic segmentation model was applied to achieve precise segmentation of tissue regions. From the segmented images, 9 morphological parameters were extracted from each of the seed coat and kernel, resulting in a total of 18 phenotypic features, which were used to construct a seed coat feature set, a kernel feature set and an integrated feature set. Variety identification was performed using 3 kinds of machine learning models: extreme learning machine (ELM), random forest (RF), and support vector machine (SVM).
The coefficients of determination between THz imaging measurements and manual measurements for the seed major axis and minor axis reached 0.830 and 0.916, respectively, verifying the high accuracy and reliability of THz technology. In terms of variety identification, the integrated feature set combined with the RF model achieved the best identification performance on the test set, with an average accuracy of 88.0%, showed significant improvement compared to models based on single-tissue features.
This study demonstrates that the THz spectral-based method enables non-destructive and accurate measurement of phenotypic traits in Citrullus lanatusv seeds and effective variety identification, providing a new technical approach for the evaluation of Citrullus lanatus germplasm resources and breeding research.
7
1
| 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 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
