CNN CLASSIFICATION OF SOYBEANS WITH STORAGE TIME BASED ON NEAR INFRARED SPECTROSCOPY

Abstract

ABSTRACT In soybeans from different years, the infrared spectroscopy waveforms exhibit similarities, yet variations in aging time lead to significant differences in their fatty acid content. To rapidly discern the year of soybean production, this study employed ten feature extraction algorithms in conjunction with Convolutional Neural Networks (CNN) to establish models for classifying soybeans from different years (2019, 2020, and 2021). The research findings reveal the following: (1) The CNN models, after feature extraction, all demonstrated improved accuracy. Notably, the optimal models for both powdered and granulated states were the Kpca-CNN models, achieving an accuracy of 100%. The corresponding loss function values were 0.0002 and 0.0007, with processing times of 0.19 seconds and 0.20 seconds, respectively. (2) The modeling results of all models suggest that the classification accuracy of soybean powder spectral data is higher compared to soybean particle spectral data. (3) Validation of the optimal Kpca-CNN model confirmed its consistent accuracy of 100% when introducing new data or reducing the size of the training dataset. In conclusion, the fusion of near-infrared spectroscopy analysis and CNN technology is considered an effective method for classifying soybeans from different years. This method provides a practical solution for the rapid and precise determination of seed production years.

Keywords

• near infrared spectroscopy
• soybeans
• storage time
• CNN