Research on the Identification Method of Maize Seed Origin Using NIR Spectroscopy and GAF-VGGNet

Xiuying Xu, Changhao Fu, Yingying Gao, Ye Kang and Wei Zhang ()
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Xiuying Xu: College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
Changhao Fu: College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
Yingying Gao: College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
Ye Kang: College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
Wei Zhang: College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China

Agriculture, 2024, vol. 14, issue 3, 1-19

Abstract: The origin of seeds is a crucial environmental factor that significantly impacts crop production. Accurate identification of seed origin holds immense importance for ensuring traceability in the seed industry. Currently, traditional methods used for identifying the origin of maize seeds involve mineral element analysis and isotope fingerprinting, which are laborious, destructive, time-consuming, and suffer from various limitations. In this experiment, near-infrared spectroscopy was employed to collect 1360 maize seeds belonging to 12 different varieties from 8 distinct origins. Spectral information within the range of 11,550–3950 cm −1 was analyzed while eliminating multiple interferences through first-order derivative combined with standard normal transform (SNV). The processed one-dimensional spectral data were then transformed into three-dimensional spectral maps using Gram’s Angle Field (GAF) to be used as input values along with the VGG-19 network model. Additionally, a convolution layer with a step size of 1 × 1 and the padding value set at 1 was added, while pooling layers had a step size of 2 × 2. A batch size of 48 and learning rate set at 10 −8 were utilized while incorporating the Dropout mechanism to prevent model overfitting. This resulted in the construction of the GAF-VGG network model which successfully decoded the output into accurate place-of-origin labels for maize seed detection. The findings suggest that the GAF-VGG network model exhibits significantly superior performance compared to both the original data and the PCA-based origin identification model in terms of accuracy, recall, specificity, and precision (96.81%, 97.23%, 95.35%, and 95.12%, respectively). The GAF-VGGNet model effectively captures the NIR features of different origins of maize seeds without requiring feature wavelength extraction, thereby reducing training time and enhancing accuracy in identifying maize seed origin. Moreover, it simplifies near-infrared (NIR) spectral modeling complexity and presents a novel approach to maize seed origin identification and traceability analysis.

Keywords: near-infrared spectroscopy; maize seed; deep learning; Gramian Angular Field; Gram matrix (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: 2024
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