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What is NIR Spectroscopy for Seed Viability Detection about?

The study explores the use of near-infrared spectroscopy (NIRS) combined with machine learning (ML) techniques for the non-destructive identification of viable seeds, addressing the limitations of traditional methods. Three ML models—Linear Discriminant Analysis, Random Forest, and Artificial Neural Networks—were evaluated, with the ANN model achieving approximately 95% accuracy in classifying seed viability. The findings suggest that integrating NIRS and ML can enhance seed conservation efforts and provide

Author

Rakesh

Language

EN