Machine Learning for Seed Quality Classification: An Advanced Approach Using Merger Data from FT-NIR Spectroscopy and...

Area of Science:

• Agricultural Science
• Biotechnology
• Data Science

Background:

• Seed quality assessment is crucial for the seed industry, impacting marketing and crop establishment.
• Traditional methods for evaluating seed germination and vigor can be time-consuming and destructive.
• Advances in optical sensors and machine learning offer non-destructive, rapid alternatives.

Discussion:

• This study introduces novel classifier models for seed quality using Fourier transform near-infrared (FT-NIR) spectroscopy and X-ray imaging.
• Various machine learning algorithms including LDA, PLS-DA, RF, NB, and SVM-r were evaluated.
• Individual models achieved high accuracy for germination prediction (up to 90% with X-ray data).

Key Insights:

• Combined FT-NIR and X-ray data improved germination prediction accuracy to 85%.
• X-ray imaging data, particularly with the LDA algorithm, demonstrated significant potential for classifying *Urochloa brizantha* seed quality.
• The developed models provide efficient, non-destructive methods for assessing seed germination capacity.

Outlook:

• Further research can explore the integration of these techniques for a wider range of crop species.
• Optimization of machine learning algorithms and sensor fusion can enhance prediction accuracy for seed vigor.
• These non-destructive methods can revolutionize seed quality control and marketing practices.