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.

In order to detect the oleic acid content of rapeseed quickly and accurately, we propose, in this paper, an artificial BP neural networks based model for predicting oleic acid content by using rapeseed’s hyperspectral information. Four types of spectral features are selected for our investigation, namely multifractal index, sensitive band, trilateral parameters, and spectral index. Both univariate variable and multiple variables are considered as our model input. The result shows that the combined feature has higher precision and better stability than when using a single parameter. An interesting finding shows that the combined feature involving multifractal parameters can significantly improve the model performance. Taking the combined feature {MF-h(0), SB-DR₅₇₄, SPI-NDSI(R₅₇₅, R₅₇₆)} as the model input, the constructed BP (back propagation) neural networks model has the highest precision, with the coefficient of determination (R²) 0.8753, root mean square error (RMSE) 1.0301, and relative error (RE) 1.047%. This result provides some experience for the rapid detection of rapeseed’s oleic acid content.

The tuberous roots of Potentilla anserina (Pan) are an edible and medicinal resource in Qinghai–Tibetan Plateau, China. The triterpenoids from tuberous roots have shown promising anti-cancer, hepatoprotective, and anti-inflammatory properties. In this study, we carried out phylogenetic analysis of squalene synthases (SQSs), squalene epoxidases (SQEs), and oxidosqualene cyclases (OSCs) in the pathway of triterpenes. In total, 6, 26, and 20 genes of SQSs, SQEs, and OSCs were retrieved from the genome of Pan, respectively. Moreover, 6 SQSs and 25 SQEs genes expressed in two sub-genomes (A and B) of Pan. SQSs were not expanded after whole-genome duplication (WGD), and the duplicated genes were detected in SQEs. Twenty OSCs were divided into two clades of cycloartenol synthases (CASs) and β-amyrin synthases (β-ASs) by a phylogenetic tree, characterized with gene duplication and evolutionary divergence. We speculated that β-ASs and CASs may participate in triterpenes synthesis. The data presented act as valuable references for future studies on the triterpene synthetic pathway of Pan.