Discover the transformative impact of Data Augmentation in Optical Spectroscopy for agrifood analysis. Learn how it enhances model accuracy, overcomes data scarcity, improves generalization, and reduces overfitting. Explore various augmentation methods, from noise injection to synthetic data generation, as detailed in a comprehensive overview published in Sensors MDPI. Uncover the key role Data Augmentation plays in streamlining data collection for robust and accurate analytical results in agrifood technology.
