Abstract Grassland is an important resource for China's economic development and the main economic source of animal husbandry. The identification and classification of grassland forage is an important part of the improvement of forage varieties and the monitoring of germplasm resources, which can fundamentally solve the problems of poor forage quality and low reproduction rate. For the problem of low accuracy of forage identification and classification, the authors put forward a new 3DSECNN model to remove the preprocessing operation and directly study the images. The authors took forage hyperspectral image (HSI) images on the field and built dataset, used 3DSECNN to train the images to improve the classification effect. The outstanding contributions of this paper are: (1) The authors took high‐precision forage HSI images in the field, established a dedicated database of forage HSIs, and expanded the datasets; (2) the process of integrating preprocessing ideas into the network and replacing the traditional method of preprocessing the data and then extracting features; (3) proposing the 3DSECNN model, which adds SENet on the basis of the traditional 3DCNN, strengthens the correlation of the spatial dimension, selects the key features for the classification by calculating the channel weight, inhibits the unimportant information, and achieves the purpose of integrating the preprocessing ideas into the network. The experimental results show the overall accuracy (OA) of 3DSECNN is 94.36%, Precision, Recall, F1‐score, Kappa, and Time also showed good levels. The experimental results prove that the 3DSECNN strengthens the correlation between image channels, enhances the performance ability of features, and provides a new method for the identification and classification research of forage.

Despite the recent advances in continuum robots for minimally invasive surgery or interventions, their applications to endovascular neurosurgery have remained technically challenging due to the difficulty of miniaturization. Aimed at enabling robotic applications to neurovascular interventions for endovascular treatments of stroke or brain aneurysms, we present a telerobotically controlled magnetic soft continuum robot capable of active steering and navigation under externally applied magnetic fields. For magnetic steering, a seven-degree-of-freedom (7-DOF) serial manipulator is employed to place an actuating magnet that can be manipulated via real-time teleoperation of the robot arm. A motorized linear drive is used to advance or retract the magnetic soft continuum robot, the distal tip of which is steered by the actuating magnet to enable endovascular navigation in the complex cerebral vasculature. We demonstrate the system's ability to guide selective navigation in different branches of cerebral arteries using anatomical models under visual feedback. We also compare the navigational performance of our system with that of a manually controlled passive guidewire and a conventional magnet-tipped guidewire. We found that the telerobotically controlled magnetic soft continuum robot allows for safer and quicker access to hard-to-reach areas in clinically challenging anatomies by enabling smooth navigation in narrow and winding pathways.

Soft active materials can generate flexible locomotion and change configurations through large deformations when subjected to an external environmental stimulus. They can be engineered to design 'soft machines' such as soft robots, compliant actuators, flexible electronics, or bionic medical devices. By embedding ferromagnetic particles into soft elastomer matrix, the ferromagnetic soft matter can generate flexible movement and shift morphology in response to the external magnetic field. By taking advantage of this physical property, soft active structures undergoing desired motions can be generated by tailoring the layouts of the ferromagnetic soft elastomers. Structural topology optimization has emerged as an attractive tool to achieve innovative structures by optimizing the material layout within a design domain, and it can be utilized to architect ferromagnetic soft active structures. In this paper, the level-set-based topology optimization method is employed to design ferromagnetic soft robots (FerroSoRo). The objective function comprises a sub-objective function for the kinematics requirement and a sub-objective function for minimum compliance. Shape sensitivity analysis is derived using the material time derivative and adjoint variable method. Three examples, including a gripper, an actuator, and a flytrap structure, are studied to demonstrate the effectiveness of the proposed framework.