In response to the issues of high honeysuckle-picking costs and low efficiency in honeysuckle picking, this study has devised a comb-brush-type picking device, considering the unique characteristics of honeysuckle plants. We elucidated the device’s structure and operational principles and designed critical components within the picking mechanism. Subsequently, through theoretical analysis, we identified the primary factors influencing the device’s operational performance. We then used the honeysuckle picking rates, honeysuckle breakage rates, and impurity rates as assessment metrics. Utilizing a one-factor test, we determined the permissible ranges for each factor. Employing the response surface methodology, we analyzed the interactions among these factors and conducted model parameter optimization. This optimization identified the optimal parameter combination: a forward speed of 3.99 km/h, a driving shaft speed of 316.53 rpm, and a picking teeth length of 70 mm. Finally, we performed verification tests using these optimized parameters. The results demonstrated that the maximum relative error between test verification values and model-optimized predictions was 4.86%. This outcome confirms that the comb-brush-type honeysuckle-picking device can meet the operational requirements of mechanized harvesting and offers valuable insights for developing harvesting devices for vine plants.

Aiming at the problems of high-power consumption and insufficient kneading and cutting of roughage in the total mixed ration mixer. In this paper, licorice stems were taken as experimental objects, the horizontal twin-shaft TMR mixer was used to carry out the experimental study. It should be as brief as possible and concise. Through the kneading and cutting process power analysis, determine the influencing factors of kneading and cutting power consumption. The auger speed, processing time and blade type were taken as experimental factors, with standard straw length rate and power consumption as indicators, Box–Behnken test with three factors and three levels was carried out, analysis of variance was performed on the test results, the results show that the significant effect of each factor on the standard grass length is processing time, blade type and auger speed in descending order. The significance of the influence on power consumption from large to small is auger speed, processing time and blade type. The response surface analysis and parameter optimization were carried out, the results show that the auger speed is 20 r/min, the processing time is 29 min, and the blade type is quincunx blade. At this time, the standard straw length was 82.634%; Power consumption 4525.815 kJ, TMR mixer performance reached the best. The results can provide a theoretical basis for the subsequent research and development of TMR mixer.

To address the challenges of non-uniform mixing in total mixed diet forage and the high power consumption of the required device, we developed a segmented spiral total mixed diet device. This development involved theoretical analysis to determine the structural parameters of the main body of the segmented spiral blades, the churn, and the creation of a test bed for the segmented spiral total mixed diet device. Taking mixing speed, mixing time, filling coefficient, and segmented spiral blade spacing as test factors and mixing uniformity and energy consumption per unit mass as test indexes, the optimal combination of operating parameters of the device was determined by using a four-factor, three-level orthogonal test method. The results of the validation test showed that the mixing uniformity of the device under these conditions was 93.41%, the energy consumption per unit mass was 4723.69 J, and the errors between the mixing test values of the device and the optimized values of the model were all less than 5%. This study can provide a reference for improving the working quality of the segmented spiral TMR mixer.

In response to the poor performance of long-distance small target recognition tasks and real-time intelligent monitoring, this paper proposes a deep learning-based recognition method aimed at improving the ability to recognize and monitor various behaviors of captive ewes. Additionally, we have developed a system platform based on ELFN-YOLO to monitor the behaviors of ewes. ELFN-YOLO enhances the overall performance of the model by combining ELFN with the attention mechanism CBAM. ELFN strengthens multiple layers with fewer parameters, while the attention mechanism further emphasizes the channel information interaction based on ELFN. It also improves the ability of ELFN to extract spatial information in small target occlusion scenarios, leading to better recognition results. The proposed ELFN-YOLO achieved an accuracy of 92.5%, an F1 score of 92.5%, and a mAP@0.5 of 94.7% on the ewe behavior dataset built in commercial farms, which outperformed YOLOv7-Tiny by 1.5%, 0.8%, and 0.7% in terms of accuracy, F1 score, and mAP@0.5, respectively. It also outperformed other baseline models such as Faster R-CNN, YOLOv4-Tiny, and YOLOv5s. The obtained results indicate that the proposed approach outperforms existing methods in scenarios involving multi-scale detection of small objects. The proposed method is of significant importance for strengthening animal welfare and ewe management, and it provides valuable data support for subsequent tracking algorithms to monitor the activity status of ewes.

In order to obtain accurate contact parameters for the discrete element simulation of salt particles used in animal husbandry, the principle of particle contact scaling and dimensional analysis were used for particle scaling. Firstly, the Plackett Burman experiment was used to screen the parameters that significantly affect the angle of repose: salt salt rolling friction coefficient, salt salt recovery coefficient, and salt steel rolling friction coefficient. Considering the influence of other parameters, a combination of bench and simulation experiments was used to calibrate the contact parameters between salt particles and steel plates used in animal husbandry in EDEM. Finally, through the stacking test, steepest climbing test, and orthogonal rotation combination test, the salt salt rolling friction coefficient was obtained to be 0.23, the salt salt recovery coefficient was 0.544, and the salt steel rolling friction coefficient was 0.368, which were verified through bench tests. The experimental results show that the relative error between the actual value of the stacking angle and the simulation results is 0.6%. The results indicate that the calibrated contact parameters can be used for discrete element simulation of salt particles for animal husbandry, providing reference for the design of quantitative feeding screws and silos.