Prediction of Maize Seed Vigor Based on First-Order Difference Characteristics of Hyperspectral Data

The identification of seed vigor is great significance to improve the germination rate, increase crop yield, and ensure product quality. In this study, based on a hyperspectral data acquisition system an improved feature extraction algorithm, model characteristics for corn seeds was constructed. research, standard test Zhengdan 958 were carried out. By integrating in spectral range 386.7–1016.7 nm first derivative information data, root length prediction successfully completed. regression relationship between seedling established by principal component regression, partial least squares, support vector regression. obtained comparing results with original which preprocessed Savitzky–Golay smoothing, multiplicative scatter correction, normal variate, curve fitting. showed that first-order differential better performance than one other processing algorithms. using different models, it found method can effectively predict seed, coefficient determination reaching 0.8319.