The Determination of Sugarbeet Seed Vigor: Based on Near Infrared High Spectrum Technology

doi:10.11924/j.issn.1000-6850.casb2021-0590

Abstract

Abstract:

To guarantee the emergence rate and production potential of sugar beet seed in field, it is necessary to test the seed vigor before planting and select the seed with high vigor for planting. The sugar beet seeds were aged by high temperature treatment, and then the intelligent detection model of seed vigor was constructed based on five near-infrared hyperspectral pretreatment methods: standard normal transformation (SNV), de-trending correction (DET), Savitzky-Golay smoothing (SG), first-order difference (1D) and second-order difference (2D), so as to obtain the predicted seed germination rate. The actual germination rate was obtained by germination test of aged beet seeds. The results show that the intelligent detection model established by the first-order differential pretreatment of sugar beet seeds has the best prediction performance, and its prediction accuracy could reach 91.92%. Comparing the actual germination rate of untreated sugar beet seeds with the prediction results of the intelligent model established by the first-order difference pretreatment method, it is found that the prediction accuracy of the model is 88.2%. It is concluded that the near infrared hyperspectral technology is a new method for rapid and nondestructive determination of crop seed vigor.

Key words: seed vigor determination, sugar beet seed, high temperature treatment, near infrared hyperspectral imaging, Gauss vector machine

CLC Number:

S566.3

Zhou Xiang, Xing Wang, Yang Jun, Sun Laijun, Wang Luhong, Zhou Wanting, Wang Xueqian, Li Siqi, Wang Man. The Determination of Sugarbeet Seed Vigor: Based on Near Infrared High Spectrum Technology[J]. Chinese Agricultural Science Bulletin, 2021, 37(29): 7-12.

Figures/Tables 4

References 32

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老化周期/d	4 d	4~8 d	不能成活	发芽率/%
2	96	126	147	60.16
4	80	94	156	52.73
6	64	175	262	47.70
总计	240	395	565	53.02

老化周期/d	4 d	4~8 d	不能成活	发芽率/%
2	96	126	147	60.16
4	80	94	156	52.73
6	64	175	262	47.70
总计	240	395	565	53.02

项目	发芽种子总数	未发芽种子总数
模型预测	252	248
发芽实验	275	225
错误预测数	23	36

项目	发芽种子总数	未发芽种子总数
模型预测	252	248
发芽实验	275	225
错误预测数	23	36