Deep learning has drawn much attention in recent years, due to its powerful feature extraction and excellent prediction. Particularly, it can also be expected for the target detection in the animal husbandry industry. The existing research can focus mainly on the cow behaviour in the outdoor farm environments. However, only relatively limited research can be found under the indoor complex scenarios. Additionally, some challenging factors have been presented by the indoor environment of farms, such as the high background similarity and severe occlusions. It is the high demand for the robustness and generalisation of the target detection models. In this study, a multi-target detection method was proposed for the daily behaviour of dairy cows in a complex environment using improved YOLO11n. Firstly, the images of the cow behaviour were collected in the real indoor environments of the farm. Four basic behaviours were conducted, including standing, walking, lying, and eating food. A multi-target cow behaviour dataset was then constructed to finely annotate the images using LabelImg software. The various scales, angles, and behavioural postures were covered for the model training. Furthermore, the bottleneck structure was optimised to design the model architecture using wavelet convolution. The bottleneck structure of the C3k2 module was reconstructed to introduce a wavelet transform domain for the feature extraction. The receptive field was effectively expanded to represent the complex background. The contextual information was significantly enhanced after reconstruction. The attention mechanism of the cascade group was integrated into the C2PSA module. A spatial-channel strategy was employed to improve the feature extraction in the occluded areas. In the feature fusion stage, the Efficient RepGFPN was utilised as the neck network, in order to effectively capture the features from the cow behaviour images at different scales. Comparative experiments were conducted to verify the performance of the improved mode in complex environments. The results show that the mean average precision of the WCG-YOLO11n was 95.3% on the daily behaviour detection of the dairy cows, which was improved by 2.2 percentage points compared with the baseline model. The mAP_0.5 increased by 2.7, 2.1, 1.3, 2.9, 2.4, and 0.6 percentage points, respectively, compared with the high floating point computation and parameter models, such as the Faster R-CNN, DETR, YOLOv5s, YOLOv7, YOLOv8n, and YOLOv9. The floating point operations, the model parameters, and the model sizes were 9.5 G, 3.9 M, and 8.3 MB, respectively, for the WCG-YOLO11n. There was a slight increase of 3.2 G, 1.3 M, and 2.8 MB, respectively, compared with the baseline model YOLO11n. However, these values were still significantly smaller than those of the high-precision models, such as the DETR, YOLOv5s, and YOLOv9. The results show that the model has a high average accuracy and processing speed while consuming fewer computing resources, and is suitable for mobile devices. Furthermore, the 23 cow behaviours were detected in the multi-behaviour, multi-scale, and dense scene scenarios. The 4 missed and 0 false detections demonstrated that the superior performance was achieved, compared with the YOLOv5s, YOLOv8n, and YOLOv11n. The excellent performance and stability of the detection were the same as the high computation and parameter models, like the Faster R-CNN, DETR, YOLOv7, and YOLOv9. The outstanding accuracy can be expected to effectively handle the varying degrees of occlusion interference. Particularly, the improved model can be deployed on mobile devices in order to detect the daily behaviours of the dairy cows in the highly occluded complex environments. This finding can provide robust technical support to monitor the cow behaviour in the large-scale farm.

Residual film resources has have been fully utilized after the removal of impurity impurities in modern agriculture. Among them, the film miscellaneous bale materials are collected by cotton field machines. Complex composition and large compact volume have posed the a great challenge on to the subsequent treatment in the field. The feeding and crushing of film miscellaneous bale materials have been analyzed to determine the main influencing factors: the diameter and rotational speed of the feed roller. Meanwhile, the primary influencing factors on the crushing of film miscellaneous bale materials were the rotational speed of the crushing knife roller, and the shape of the crushing tool. A technical solution has been proposed to firstly unpack and disperse the film miscellaneous bale materials, and then crush the unpacked material into appropriate granularities. In this study, a multi-stage crusher was designed for the film miscellaneous bale materials, including a uniform feeding, a straight blade crushing, and a Y-type flailing blade crushing device. Among them, the uniform feeding device was used to feed the materials into the crushing device at a uniform speed during feeding. The high-speed rotating crushing device was to prevent from pulling a large amount of film miscellaneous bale materials into the crushing chamber, leading to the blockage. At the same time, a chute was equipped in the uniform feeding device, in order to adaptively adjust the height of the feed roller, according to the different sizes of film miscellaneous bale material. The straight blade crushing device was to break the film miscellaneous bale materials. The complex composition was simplified into a separate crushing, in order to improve the crushing quality. The Y-type flailing blade crushing device was to crush the materials beyond the standard size for the second time, and then throw the qualified materials out of the crushing chamber. The blade roller speed, feeding speed, and the number of fixed knives were taken as the main influencing factors, while the qualified rate of residual film crushing was as the evaluation index. A three-factor and three-level experiment was conducted to manually sort the materials after the experiment. The qualified rate of residual film crushing was calculated after size classification. The software Design Expert was used to determine the optimization parameter index. The reasons were explored for the impact trends of three factors. A mathematical model was established to clarify the effect of three factors on the qualified rate of film residue crushing. The significant order of each experimental factor on the qualified rate of residual film crushing was ranked in the descending order of the blade roller speed, number of fixed knives, feeding speed. Experimental verification was also carried out at a rotational speed of 1 125 r/min of the crushing knife roller. The feeding speed was 0.012 m/s at the factor level of one fixed number of knives, while the qualified rate of film residue crushing reached 75.15%. The improved model was reliable to fully meet the requirements of subsequent processing. The finding can provide the important significance and key application for the residual film resource utilization.

Few studies have been carried out on special shredding technology and equipment, hence it is hard to find out the characteristic rule of distribution of residual films after shredding. By evaluating the mechanical properties of the residual film during the cutting process of a mixture of mechanically recovered residual film and impurities, the main parameters influencing the film distribution feature were acquired. Physical tests were conducted on the basis of a multi-blade toothed shredding device. A relationship model between the film distribution feature and main parameters was constructed through the central grouping method and regression analysis of variance, aiming to investigate the influence rule of main parameters on the film distribution feature and the interaction between them, and to obtain the optimal combination of parameters for the cutting device. The difference between the experimental validation value and the model prediction ranged from 1.03% to 9.56%, which showed that the model has reliable and accurate predictive ability.