The seed maturity, which is one of the important factors that affect seed vigor, is an important quality index. During seed sorting, separating mature seeds from immature seeds can improve the vigor of seed lot and keep vigor consistency. Hyperspectral imaging that covered the range of 400~1 000 nm was used to find out the sensitive bands reflecting corn seed maturity, and corresponding images were employed to classify the immature corn seeds. Principal component analysis (PCA) algorithm was adopted to analyze the hyperspectral image. PC2 of PCA had the greatest difference between immature and mature areas on the seeds, therefore, the weighted coefficients of PC2 was selected to extract sensitive wavebands (501 nm). Regions of interest (ROI) from mature and immature area of 70 immature kernels was selected for mean spectra calculation. Partial least square regression (PLSR) algorithm was employed to analyze the spectra of ROI and extract wavelength related to maturity (518 nm). Band ratio algorithm and Kruskal-Wallis test were used to select the best band ratio that had the biggest difference between mature and immature areas (640 nm/525 nm). 864 kernels of corn seed were analyzed by gray images of the selected wavelengths as well as band ratio images. Results showed that the light color regions of the seed crown were misidentified as immature region when the images of selected single band wavelengths were used, while the band ratio image of 640 nm/525 nm could be identified correctly. The immature seeds can be separated from the mature seeds according to the area ratio of segmented immature region to the whole kernel. The correct recognition rate was 93.9%. Using the grey images of selected band ratio can differentiate immature corn seeds from mature seeds effectively, which provide a theoretical reference for the development of seed sorting device in further work.
Algorithms Least-Squares Analysis