Physical dormancy (hard seed) occurs in most species of Leguminosae family and has great consequences not only for ecological adaptation but also for agricultural practice of these species. A rapid, nondestructive and on-site screening method to detect hard seed within species is fundamental important for maintaining seed vigor and germplasm storage as well as understanding seed adaptation to various environment. In this study, the potential of multispectral imaging with object-wise multivariate image analysis was evaluated as a way to identify hard and soft seeds in , , , , , and . Principal component analysis (PCA), linear discrimination analysis (LDA), and support vector machines (SVM) methods were applied to classify hard and soft seeds according to their morphological features and spectral traits.The performance of discrimination model via multispectral imaging analysis was varied with species. For , , and , an excellent classification could be achieved in an independent validation data set. LDA model had the best calibration and validation abilities with the accuracy up to 90% for . SVM got excellent seed discrimination results with classification accuracy of 91.67% and 87.5% for and , respectively. However, both LDA and SVM model failed to discriminate hard and soft seeds in , , and .Multispectral imaging together with multivariate analysis could be a promising technique to identify single hard seed in some legume species with high efficiency. More legume species with physical dormancy need to be studied in future research to extend the use of multispectral imaging techniques.