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.

A_VNDVI (Accumulative Visible Normalized Difference Vegetation Index), a new type of derivative parameters of NDVI, was set up by improving the computational formulas and importing the spectral information of visible bands after analyzing the construction idea of NDVI and its derivative parameters. Then, the characteristic values of VNDVI (Visible NDVI) were calculated by applying a combinational method of sensitive bands of visible bands. The study carried out the fitting analysis between NDVI, VNDVI, A_VNDVI, and LAI (Leaf Area Index). Several conclusions are obtained according to data analysis. Firstly, all of the determination coefficients between NDVI, VNDVI, A_VNDVI, and LAI of rapeseed can reach or exceed 0.83. The distribution of their RMSE values ranges from 0.4 to 0.5 and absolute values of RE vary from 0.9% to 2.1%. Secondly, the inversion sensitivity S_V of VNDVI and LAI ranges from 0.7 to 1.9 relative to NDVI, and the inversion sensitivity S_A of A_VNDVI decreases in varying degrees with the promotion of capacity of resisting disturbance accordingly. Its value varies from 0.1 to 0.9. Thirdly, the values of S_A remain stable between 0.1 and 0.3 with the increase of NDVI. Applying the inversion model of A_VNDVI will be a considerable scheme when faced with a complex environment and many interfering factors.

To reduce the error and faster classification by mechanizing in classifying the potato shape and size through machine vision using the extraction of characters procedure to identify the size, and using the shape detection procedure to identify the shape. Test results in potato size detection revealed 40/191 = 0.210mm/pixel as length scale or calibration factor (40/M) where 40 is the table tennis ball size (40mm) and 191 as image pixels table tennis (M); measurement results revealed that between the algorithm results and the manual measurements, the absolute error was <3mm, while the relative error rate was <4%; and the measurement results based on the ellipse axis length can accurately calculate the actual long axis and short axis of potato. Potato shape detection revealed the analysis of 228 images composed of 114 positive and 114 negatives side, only 2 have been incorrectly classified, mainly because the Extracted ratio (R) of the potato image of those two positive and negative images are near 0.67, respectively 0.671887, 0.661063, 0.667604, and 0.67193. The comparison to establish a calibration system method using both basic rectangle and ellipse R ratio methods to detect the potato size and shape, revealed that the basic rectangle method has better effect in the case of fixed place. Moreover, the ellipse axis method was observed to be more stable with an error rate of 7%. Therefore it is recommended that the ellipse axis method should be used to detect the shape of potato for differentiation into round, long cylindrical, and oval shapes, with the accuracy level of 98.8%.

In this paper, we first introduced the development of the international E-learning standards investigation, and then we made a brief comparison among the comprehensive ones. Afterwards, we choose the SCORM standards as the representation to make deeper study because of its rapid and leading development.