Addressing the issues of the sand cat swarm optimization algorithm (SCSO), such as its weak global search ability and tendency to fall into local optima, this paper proposes an improved strategy called the multi-strategy integrated sand cat swarm optimization algorithm (MSCSO). The MSCSO algorithm improves upon the SCSO in several ways. Firstly, it employs the good point set strategy instead of a random strategy for population initialization, effectively enhancing the uniformity and diversity of the population distribution. Secondly, a nonlinear adjustment strategy is introduced to dynamically adjust the search range of the sand cats during the exploration and exploitation phases, significantly increasing the likelihood of finding more high-quality solutions. Lastly, the algorithm integrates the early warning mechanism of the sparrow search algorithm, enabling the sand cats to escape from their original positions and rapidly move towards the optimal solution, thus avoiding local optima. Using 29 benchmark functions of 30, 50, and 100 dimensions from CEC 2017 as experimental subjects, this paper further evaluates the MSCSO algorithm through Wilcoxon rank-sum tests and Friedman’s test, verifying its global solid search ability and convergence performance. In practical engineering problems such as reducer and welded beam design, MSCSO also demonstrates superior performance compared to five other intelligent algorithms, showing a remarkable ability to approach the optimal solutions for these engineering problems.

To address the low efficiency and stress-inducing nature of traditional manual weighing for dairy cows, this study proposes a semantically guided 3D reconstruction and body weight estimation method for dairy cows. First, a dual-viewpoint Kinect V2 camera synchronous acquisition system captures top-view and side-view point cloud data from 150 calves and 150 lactating cows. Subsequently, the CSS-PointNet++ network model was designed. Building upon PointNet++, it incorporates Convolutional Block Attention Module (CBAM) and Attention-Weighted Hybrid Pooling Module (AHPM) to achieve precise semantic segmentation of the torso and limbs in the side-view point cloud. Based on this, point cloud registration algorithms were applied to align the dual-view point clouds. Missing parts were mirrored and completed using semantic information to achieve 3D reconstruction. Finally, a body weight estimation model was established based on volume and surface area through surface reconstruction. Experiments demonstrate that CSS-PointNet++ achieves an Overall Accuracy (OA) of 98.35% and a mean Intersection over Union (mIoU) of 95.61% in semantic segmentation tasks, representing improvements of 2.2% and 4.65% over PointNet++, respectively. In the weight estimation phase, the BP neural network (BPNN) delivers optimal performance: For the calf group, the Mean Absolute Error (MAE) was 1.8409 kg, Root Mean Square Error (RMSE) was 2.4895 kg, Mean Relative Error (MRE) was 1.49%, and Coefficient of Determination (R²) was 0.9204; for the lactating cows group, MAE was 12.5784 kg, RMSE was 14.4537 kg, MRE was 1.75%, and R² was 0.8628. This method enables 3D reconstruction and body weight estimation of cows during walking, providing an efficient and precise body weight monitoring solution for precision farming.

To address the problem of large tractive resistance in traditional subsoiling methods, this paper designed a pulse-type gas explosion subsoiler, as well as an air-blown double-ended chisel type subsoiling shovel and a conduit. The mathematical equation of the influence of the structural parameters of the subsoiler on the groove profile is established. The EDEM 2022 software was used to simulate the subsoiling operation process. The soil disturbance law of the chisel subsoiler was analyzed by the change of soil particle velocity. The optimum value interval of quadratic regression orthogonal rotation combination test factors was determined by using the steepest climb test, with specific tillage resistance and filling power as evaluation indicators. Based on the Box–Behnken design test, a second-order regression model of response value and significance parameter was obtained, and an optimal combination was found by optimizing the significance parameter. The effects of subsoiling air pressure, pulse width and pulse interval on evaluation indicators were analyzed by the response surface method; the test results show that when the air pressure was 0.8 MPa, the pulse width was 0.17 s and the pulse interval was 0.12 s, and the specific tillage resistance was 0.4421 N/mm² and the filling power was 18.5%; a comparative test between the pulse-type gas explosion subsoiler and a continuous gas explosion subsoiler was carried out, and the specific tillage resistance was reduced by 12.2% and the filling power was reduced by 10.5%; the comparative test shows that the pulse-type gas explosion subsoiler has smaller tractive resistance per unit area and smaller disturbance to soil. The research results provide a theoretical basis and reference for the optimization and improvement of gas explosion subsoilers.

This study aimed to enhance the efficiency of tiger nut combine harvesters by reducing impurity and loss rates during processing. Scholars focused on analyzing the composition and suspension speed of the bean mixture, leading to the development of a wind-screen impurity-removal method. The wind-screen-type bean-separation device was designed with a cross-flow fan, louver screen, frame, and driving mechanism. Theoretical analysis was employed to discuss the motion characteristics and behavior of the sieve body and material, thereby revealing the screening dynamics of tiger nuts and impurities. Factors such as crank radius, crank speed, and fan speed were identified as crucial for optimizing separation performance. Initial single-factor tests helped narrow down the range of influencing factors. Subsequently, a three-factor, three-level Box–Behnken test was conducted with crank radius, crank speed, and fan speed as variables and impurity rate and loss rate as evaluation indexes. This led to the establishment of a multiple regression equation linking these factors to the evaluation indexes. Through response surface analysis and multi-objective optimization using the regression model, the optimal operational parameters for the device were determined: crank radius of 45 mm, crank speed of 497 r/min, and the fan speed of 1100 r/min. Theoretical calculations predicted an impurity rate of 2.42% and a loss rate of 0.51%. Verification tests confirmed these findings, showing an average impurity rate of 2.53% and a loss rate of 0.56%, which met the mechanized harvesting standards for tiger nuts. Overall, this study introduces a novel method and technical framework for effectively separating tiger nuts from impurities, thereby advancing the mechanization of tiger nut harvesting processes.