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.

Rapeseed Seed Coat Color Classification Based on the Visibility Graph Algorithm and Hyperspectral Technique

“…Different objects or surface materials exhibit distinct spectral reflectance in different bands, forming a unique spectral fingerprint. Leveraging spectral fingerprints allows for the effective differentiation of species types or prediction of surface properties and chemical compositions [13,[56][57][58]. The complex network is a powerful tool used to understand the topological and dynamic characteristics of complex systems [34,38,41,59].…”

“…Hyperspectral imaging technology can simultaneously obtain spectral information from multiple bands, providing richer spectral information (including images and data) and characterizing the inherent features of substances. Due to its high resolution and comprehensive spectrum, it has achieved successful applications in many fields such as agriculture, geology, and environment [11,12], for example, the identification of rapeseed varieties and the prediction of oleic acid, protein, and other components by hyperspectral [13,14].…”

Imaging Hyperspectral Feature Fusion for Estimation of Rapeseed Pod’s Water Content and Recognition of Pod’s Maturity Level

“…This integrative approach has emerged as an invaluable diagnostic tool for plant phenotyping and growth monitoring. In recent years, numerous studies have leveraged hyperspectral imaging to assess plant physiological status through spectral information analysis [13][14][15][16][17][18], with a burgeoning focus on water content determination [19][20][21][22][23][24], such as Sun et al [22] who developed a hyperspectral method for accurate, rapid lettuce leaf water content detection during growth. Applying the CARS-ABC-SVR model optimized using an artificial bee colony algorithm, the R 2 is found to be 0.9214 and the RMSE is 2.95% on the prediction set.…”

The prediction model of nitrogen nutrition in cotton canopy leaves based on hyperspectral visible‐near infrared band feature fusion

“…It showed that the multifractal parameters exacted from the proposed sensitive band brought better mode performance. Liu et al [14] proposed a rapeseed recognition model by fusing multiple hyperspectral features, one of which was the multifractal features obtained by the MF-DFA. The result showed that the new fusion features helped to improve model accuracy significantly.…”