| Autor: |
Yunting Hui, Decheng Wang, Yong You, Xin Tang, Yaoqi Peng, Lu Zhu, Xiaolong Huan |
| Předmět: |
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| Zdroj: |
Crop Science; Nov/Dec2021, Vol. 61 Issue 6, p4284-4292, 9p |
| Abstrakt: |
The high quality of sorghum × sudangrass [Sorghum bicolor (L.) Moench. × S. sudanense (Piper) Stapf.] seed is an important prerequisite for its application in animal husbandry, and germination percentage is one of the most routine indicators used to test seed quality. This study proposes a method for the rapid and nondestructive measurement of sorghum × sudangrass seed germination percentage based on multispectral image technology. We constructed target region in sorghum × sudangrass seed samples, and after white board calibration and ratio conversion, the spectral reflectance of each group of seeds was obtained at five wavebands. A seed germination test was performed in an incubator, and germination percentage was obtained from 100 sorghum × sudangrass seed samples. Using the neural network and the Levenberg-Marquardt method, spectral reflectance and germination percentage data from the 100 seed samples were used to establish a predictivemodel of seed germination percentage. The input neurons were reflectance in five wavelength bands, and the output neuron was seeds germination percentage. Experimental data from 80 samples were randomly selected for training, and data from the remaining 20 nontraining samples were imported into the predictive model for simulation verification. The fitting correlation coefficient of themodel was .73202, representing the relevant closing degree, and the correlation coefficient between the predicted value and the simulation value from 20 nontraining samples was .7533, which referred to the relationship between variables. The model was able to predict the seed germination percentage with acceptable accuracy. Therefore, the nondestructive method described here may be suitable for rapid detection of sorghum × sudangrass seed germination percentage in the context of seed production. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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