Aiming at the lack of a precise discrete element model in the simulation analysis of corn straw crushing, this paper established bond models with different structure and particle models with different properties of corn straw based on Hertz-Mindlin with bonding, and verified the discrete element model by combining physical test and simulation optimization design methods. Firstly, this study generates the bonded particle models based on Hertz-Mindlin with bonding for epidermis-epidermis, inner flesh-internal flesh, and epidermis-internal flesh, respectively. The bonding parameters of the model are calibrated with the help of a bending damage test, bending damage simulation test, and actual test with 2 mm/min speed. It shows that the maximum destructive force errors of the epidermis and inner flesh are 2.4% and 1.6%, respectively. On this basis, a discrete element model of corn straw is established by combining the calibrated parameters of the bonding parameters of epidermis—epidermis, inner flesh—inner flesh, and epidermis—inner flesh bond. The bending failure test shows that the mechanical properties of corn straw are similar between the simulated test and the actual test, and the maximum destructive power is 288 N and 292 N, respectively. The relative error is 1.36%. The feasibility of the discrete element model for simulation analysis is verified, which shows that the established discrete element model can be applied to the simulation analysis of corn straw cutting and crushing process.

Accurate identification of individual goat identity is necessary for precision farming. Previous studies have primarily focused on using front face images for goat identification, leaving the potential of other appearances and multi-source appearance fusion unexplored. In this study, we used a self-developed multi-view appearance image acquisition platform to capture five different appearances (left face, right face, front face, back body, and side body) from 54 Wanlin white goats. The recognition ability of different goat appearance images and its multi-source appearance fusion for its identity recognition was then systematically examined based on the four basic network models, namely, MobileNetV3, MobileViT, ResNet18, and VGG16, and the best combination of goat appearance and network was screened. When only one kind of goat appearance image was used, the combination of side body image and MobileViT was the best, with an accuracy of 99.63%; under identity recognition based on multi-source image appearance fusion, all recognition models after outlook fusion of two viewpoints generally outperformed single viewpoint appearance identity recognition models in recognizing the identity of individual goats; when three or more kinds of goat appearance images were utilized for fusion, any of the four models were capable of identifying the identity of an individual goat with 100% accuracy. Based on these results, a goat individual identity recognition strategy was proposed that balances accuracy, computation, and time, providing new ideas for goat individual identity recognition in complex farming contexts.