Abstract
Seed counting and broken seed identification are important tasks in evaluating seed quality. In this study, we proposed a computational method designed to perform these two functions. Image sequences of soybean seeds during falling were collected, and their morphologies were examined from different views. An a priori clustering algorithm composed of a support vector machine and k-means clustering algorithm was used to segment touching seeds within images. The morphologies of specific soybean seeds in sequential images were associated based on a forced neighbor association criterion to avoid repeated counting and obtain shape features from multiple views. Based on the areas in different views, the basic shape features in the initial multi-view shape features were sorted to obtain the guided multi-view shape features. The support vector machine was used with the guided multi-view shape feature to classify seeds as intact or broken. The experimental results show that the proposed a priori clustering algorithm accurately segmented touching seeds. The forced nearest-neighbor data association algorithm is insensitive to touching seeds and achieved highly accurate seed counting. Compared with the single-view shape feature, the multi-view shape feature significantly improves the accuracy of seed morphological classification. The proposed method exhibited considerable potential for applications in agricultural engineering.
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