Crushing is very essential to effective initial cleaning, one of the primary steps in residual film recycling. Crushing equipment has been developed to efficiently break down the film impurities for subsequent cleaning. Existing crushing devices of residual film can often effectively process the loose film and similar materials. But the residual film recovery has shifted from the loose materials to bale recovery in recent years. Furthermore, the film stray round bale is more compact than the bulk material wrapping after the recovery. The mechanical properties are quite different than before. However, it is very limited to the crushing efficiency of the whole bundle of film stray round bale. The large volume, compact winding, and mechanical properties are required to effectively break the whole bundle in the resource utilization of the residual film. In this study, a single-roller square blade crushing device for machine-harvested film-impurities round bale in cotton field was designed to improve the quality of recycled products using sliding cutting. According to the overall structure and working principle, the material properties of the film receiving mixed round bale were combined with the theoretical analysis of the crushing process of the device. The influencing factors were obtained on the crushing quality, including the pushing speed of the push board, the rotating speed of the moving blade roller, and the vertical height of the moving blade roller and the lower blade. The structure and working parameters of the crushing device were also determined, including the curve and shape of the moving and fixed blade edge, the outer diameter of the moving blade roller, three kinds of arrangement mode of the moving blade, and the clearance between the moving and lower blade. The maximum outer dimension of the broken residual film after unfolding was divided into four distribution ranges for sorting and weighing. In the material classification, the materials with the smaller dimensions and those without collection were all classified into the minimum size ranges. The residual films with the maximum outer dimension within the range of [0,100) mm and ≥500 mm were defined as the over-broken film and over-long film, respectively. These two kinds of residual film were unqualified, while those within the range of [100,300) mm and [300,500) mm were defined as the qualified residual film. The percentage of qualified residual film mass in the sample was the qualified rate of residual film breakage. A three-factor and three-level response surface test was carried out with the qualified rate of broken residual film as the test evaluation index. A systematic analysis was implemented on the influence of each test factor on the performance of the device. A regression model was established for the clearance between the moving blade and the lower blade, the arrangement of the moving blade, the rotational speed of the moving blade roller, and the qualified rate of broken residual film. The optimal combination of parameters was obtained in the device, according to the parameter optimization function of Design-Expert software. The test results show that the main and secondary influencing factors on the pass rate of residual film crushing were the rotating speed of the moving blade roller, the clearance between the moving blade and the lower blade, and the arrangement of the moving blade. Specifically, the clearance between the moving blade and the lower blade was 14 mm, the moving blade arrangement was staggered, the moving blade roller speed was 165 r/min, and the qualified rate of broken residual film reached the maximum of 69.51%. The average qualified rate of broken residual film was 70.78% after the verification test, where the prediction error of the model was within 2%. The device can be expected to realize the whole bundle broken of film and miscellaneous bundle materials. The crushing quality can fully meet the requirements of film miscella resource utilization. The finding can provide a strong reference to the breaking device for the film and miscellaneous bundle materials in the cotton field.

Livestock farming is ever increasing in production scale and automation in recent years. Accurate and efficient monitoring of individual livestock can greatly contribute to the growth and health status of animals. Behavioral and health information can often be involved in the individual livestock. Among them, the lameness behavior of a cow can indirectly represent the health status of its legs. Accurate leg tracking is very critical to detect the lameness of a cow. However, the existing intelligent detection of cow lameness is often used at the milking aisle of the farm. There are the occlusion, blurring, and deformation of the leg in this viewpoint. In this study, leg tracking and lameness detection were proposed for dairy cows in the occlusion environment. Firstly, the video of cows walking was recorded and then pre-processed in an actual farming setting. 50 video segments were chosen as the samples for the multi-target tracking dataset of cow legs. Each segment was featured as a walking cow with a duration of 8-20 s. The dataset included a total of 27 healthy and 23 lame cows. In the dataset samples, the training and testing sets were randomly divided in the ratio of 8:2. 40 videos for model training, and 10 videos were used for model testing. Secondly, the improved FairMOT target tracking model of the cow leg was designed to obtain the cow leg trajectory. Specifically, the coordinate attention (CA) module and Squeeze-Excitation attention (SE) module were introduced into the FairMOT Iterative Deep Aggregation (IDA) and Hierarchical Deep Aggregation (HDA), respectively. The important features of the cow's leg were focused on reducing the interference of redundant information under shape mutation and blurring along the spatial and channel directions. NSA Kalman filtering was introduced into the trajectory state for the updating phase, in order to obtain more accurate motion states of leg targets. Tracking performance was improved to reduce the measurement weights of low-confidence detection targets when the leg occlusion occurred; the AFLink linking mechanism was introduced into the tracking post-processing stage, in order to improve the tracking performance of the model. The curve-fitting of cow movement trajectory was also designed in the cow gait cycle under the occlusion environment. Finally, the individual thresholds were used to classify the phases of the cow's gait movement. Six gait features were extracted, namely, stride length, total gait period, support phase gait period, swing phase gait period, support phase share, and swing phase share. The Navie Bayes model was then utilized for the detection and classification of limping behavior. The experimental results show that the improved model shared better localization accuracy and tracking, compared with the original FairMOT model. Specifically, the MOTA (Multiple Object Tracking Accuracy) was improved by 3.79 percentage points, whereas, the MOTP (Multiple Object Tracking Precision) and IDs (Total Target ID Switching The total number of identity switches) decreased by 0.022 and 35 times, respectively. The improved model outperformed the existing mainstream multi-target tracking models. In addition, CowMOT shared the more accurate target localization more suitable for tracking the movement of a cow's leg; The classification accuracy of lameness detection was 86.67%, indicating better defection on the lameness in dairy cattle. The finding can also provide a strong reference to intelligent monitoring of cow lameness behavior.