In this paper, reverse engineering technique was employed to extract the ridges of the hoof ball contour, and hoof ball tissue structure was analyzed based on the bionic prototype of goat hooves. The quantified geometric features were used to design the bionic track shoe pattern, which can enhance its adhesive performance and solve the problem that agricultural tracked vehicles in hilly and mountainous areas are prone to slip due to poor adhesive performance. The monolithic structure of the biomimetic goat hoof track shoe pattern and the ordinary one-line track pattern were arranged and combined; they included six kinds of track shoe models and the adhesive performance was compared. A discrete element system was established based on soil parameter determination to compare the maximum adhesion of different track shoe models. The bionic track shoe samples were prepared for soil bin tests to verify the reliability of the discrete element analysis results. Compared with the ordinary track shoe, the adhesion of the optimal bionic track shoe was improved by 9.1%.

To meet rapid and non-destructive identification of selenium-enriched agricultural products selenium-enriched millet and ordinary millet were taken as objects. Image regions of interest (ROI) were selected to extract the spectral average value based on hyperspectral imaging technology. Reducing noise by the Savitzky-Golay (SG) smoothing algorithm, variables were used as inputs that were screened by successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), CARS-SPA, UVE-SPA, and UVE-CARS, while sample variables were used as outputs to build support vector machine (SVM) models. The results showed that the accuracy of CARS-SPA-SVM was 100% in the training set and 99.58% in the test set equivalent to that of CARS-SVM and UVE-CARS-SVM, which was higher than that of SPA-SVM, UVE-SPA-SVM, and UVE-SVM. Therefore, the method of CARS-SPA had superiority, and CARS-SPA-SVM was suitable to identify selenium-enriched millet. Finally, 454.57 nm, 484.98 nm, 885.34 nm, and 937.1 nm, which were obtained by wavelength extraction algorithms, were considered as the sensitive wavelengths of selenium information. This study provided a reference for the identification of selenium-enriched agricultural products.