Vigor Detection for Naturally Aged Soybean Seeds Based on Polarized Hyperspectral Imaging Combined with Ensemble Learning Algorithm

Qingying Hu, Wei Lu (), Yuxin Guo, Wei He, Hui Luo and Yiming Deng
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Qingying Hu: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China
Wei Lu: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China
Yuxin Guo: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China
Wei He: College of Engineering, Nanjing Agricultural University, Nanjing 210095, China
Hui Luo: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China
Yiming Deng: College of Engineering, Michigan State University, East Lansing, MI 48823, USA

Agriculture, 2023, vol. 13, issue 8, 1-18

Abstract: To satisfy the increasing demand for soybeans, identifying and sorting high-vigor seeds before sowing is an effective way to improve the yield. Polarized hyperspectral imaging (PHI) technology is here proposed as a rapid, non-destructive method for detecting the vigor of naturally aged soybean seeds. First, the spectrum of 396.1–1044.1 nm was collected to automatically extract the region of interest (ROI). Then, first derivative (FD), Savitzky–Golay (SG), multiplicative scatter correction (MSC), and standard normal variate (SNV) preprocessed hyperspectral and polarized hyperspectral data (0°, 45°, 90°, and 135°) for the soybean seeds was obtained. Finally, the seed vigor prediction model based on polarized hyperspectral components such as I, Q, and U was constructed, and partial least squares regression (PLSR), back-propagation neural network (BPNN), generalized regression neural network (GRNN), support vector regression (SVR), random forest (RF), and blending ensemble learning were applied for modeling analysis. The results showed that the prediction accuracy when using PHI was improved to 93.36%, higher than that for the hyperspectral technique, with a prediction accuracy up to 97.17%, 98.25%, and 97.55% when using the polarization component of I, Q, and U, respectively.

Keywords: polarized hyperspectral imaging (PHI); natural aging; soybean seed vigor; polarization; hyperspectral (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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