Rapeseed Variety Recognition Based on Hyperspectral Feature Fusion

Abstract

As an important oil crop, rapeseed contributes to the food security of the world. In recent years, agronomists have cultivated many new varieties, which has increased human nutritional needs. Variety recognition is of great importance for yield improvement and quality breeding. In view of the low efficiency and damage of traditional methods, in this paper, we develop a noninvasive model for the recognition of rapeseed varieties based on hyperspectral feature fusion. Three types of hyperspectral image features, namely, the multifractal feature, color characteristics, and trilateral parameters, are fused together to identify 11 rapeseed species. An optimal feature is selected using a simple rule, and then the three kinds of features are fused. The support vector machine kernel method is employed as a classifier. The average recognition rate reaches 96.35% and 93.71% for distinguishing two species and 11 species, respectively. The abundance test model demonstrates that our model possesses robustness. The high recognition rate is almost independent of the number of modeling samples and classifiers. This result can provide some practical experience and method guidance for the rapid recognition of rapeseed varieties.