An antibacterial packaging film based on amylose starch with quaternary ammonium salt chitosan and its application for meat preservation.

A new generation of food packaging films is gradually replacing traditional plastic packaging films because of their biodegradability, safety, and some functional properties such as anti-bacterial and oxidant resistance. In the present work, an antibacterial packing film based on amylose starch and 2-hydroxypropyl-trimethylammonium chloride chitosan (HTCC) was prepared for meat preservation. The interfacial bonding mechanism between amylose, HTCC, and glutaraldehyde (GA) was determined experimentally and through molecular dynamics (MD) simulation. The macromolecular chains of amylose starch and HTCC became entangled via inter-molecular H-bonds and then cross-linked with GA via the Schiff base reaction. The interaction of amylose starch and HTCC improved the mechanical properties of the amylose films. Compared with the amylose films, the tensile strength and elongation at break of the optimal HTCC/amylose films reached to 16.13 MPa (an increase of 206.65 %) and 53.86 % (an increase of 109.49 %). The HTCC/amylose films were found to provide obvious bacteriostatic performance, a relatively low cytotoxicity, the lower transmittance in the UV region, and thus the ability to enhance the preservation of fresh meat. These excellent characteristics therefore suggest that HTCC/amylose films might be promising candidates for application in antibacterial food packaging films.

Regulation of Meat Duck Activeness through Photoperiod Based on Deep Learning.

The regulation of duck physiology and behavior through the photoperiod holds significant importance for enhancing poultry farming efficiency. To clarify the impact of the photoperiod on group-raised duck activeness and quantify duck activeness, this study proposes a method that employs a multi-object tracking model to calculate group-raised duck activeness. Then, duck farming experiments were designed with varying photoperiods as gradients to assess this impact. The constructed multi-object tracking model for group-raised ducks was based on YOLOv8. The C2f-Faster-EMA module, which combines C2f-Faster with the EMA attention mechanism, was used to improve the object recognition performance of YOLOv8. Furthermore, an analysis of the tracking performance of Bot-SORT, ByteTrack, and DeepSORT algorithms on small-sized duck targets was conducted. Building upon this foundation, the duck instances in the images were segmented to calculate the distance traveled by individual ducks, while the centroid of the duck mask was used in place of the mask regression box's center point. The single-frame average displacement of group-raised ducks was utilized as an intuitive indicator of their activeness. Farming experiments were conducted with varying photoperiods (24L:0D, 16L:8D, and 12L:12D), and the constructed model was used to calculate the activeness of group-raised ducks. The results demonstrated that the YOLOv8x-C2f-Faster-EMA model achieved an object recognition accuracy (mAP@50-95) of 97.9%. The improved YOLOv8 + Bot-SORT model achieved a multi-object tracking accuracy of 85.1%. When the photoperiod was set to 12L:12D, duck activeness was slightly lower than that of the commercial farming's 24L:0D lighting scheme, but duck performance was better. The methods and conclusions presented in this study can provide theoretical support for the welfare assessment of meat duck farming and photoperiod regulation strategies in farming.

Pose Estimation and Behavior Classification of Jinling White Duck Based on Improved HRNet.

In breeding ducks, obtaining the pose information is vital for perceiving their physiological health, ensuring welfare in breeding, and monitoring environmental comfort. This paper proposes a pose estimation method by combining HRNet and CBAM to achieve automatic and accurate detection of duck's multi-poses. Through comparison, HRNet-32 is identified as the optimal option for duck pose estimation. Based on this, multiple CBAM modules are densely embedded into the HRNet-32 network to obtain the pose estimation model based on HRNet-32-CBAM, realizing accurate detection and correlation of eight keypoints across six different behaviors. Furthermore, the model's generalization ability is tested under different illumination conditions, and the model's comprehensive detection abilities are evaluated on Cherry Valley ducklings of 12 and 24 days of age. Moreover, this model is compared with mainstream pose estimation methods to reveal its advantages and disadvantages, and its real-time performance is tested using images of 256 × 256, 512 × 512, and 728 × 728 pixel sizes. The experimental results indicate that for the duck pose estimation dataset, the proposed method achieves an average precision (AP) of 0.943, which has a strong generalization ability and can achieve real-time estimation of the duck's multi-poses under different ages, breeds, and farming modes. This study can provide a technical reference and a basis for the intelligent farming of poultry animals.

Research on Automatic Classification and Detection of Mutton Multi-Parts Based on Swin-Transformer.

In order to realize the real-time classification and detection of mutton multi-part, this paper proposes a mutton multi-part classification and detection method based on the Swin-Transformer. First, image augmentation techniques are adopted to increase the sample size of the sheep thoracic vertebrae and scapulae to overcome the problems of long-tailed distribution and non-equilibrium of the dataset. Then, the performances of three structural variants of the Swin-Transformer (Swin-T, Swin-B, and Swin-S) are compared through transfer learning, and the optimal model is obtained. On this basis, the robustness, generalization, and anti-occlusion abilities of the model are tested and analyzed using the significant multiscale features of the lumbar vertebrae and thoracic vertebrae, by simulating different lighting environments and occlusion scenarios, respectively. Furthermore, the model is compared with five methods commonly used in object detection tasks, namely Sparser-CNN, YoloV5, RetinaNet, CenterNet, and HRNet, and its real-time performance is tested under the following pixel resolutions: 576 × 576, 672 × 672, and 768 × 768. The results show that the proposed method achieves a mean average precision (mAP) of 0.943, while the mAP for the robustness, generalization, and anti-occlusion tests are 0.913, 0.857, and 0.845, respectively. Moreover, the model outperforms the five aforementioned methods, with mAP values that are higher by 0.009, 0.027, 0.041, 0.050, and 0.113, respectively. The average processing time of a single image with this model is 0.25 s, which meets the production line requirements. In summary, this study presents an efficient and intelligent mutton multi-part classification and detection method, which can provide technical support for the automatic sorting of mutton as well as for the processing of other livestock meat.

Thermal manipulation modifies embryonic growth, hepatic free amino acid concentrations, and hatching performance in layer-type chicks.

Thermal manipulation (TM) of incubation temperature has been demonstrated to alter metabolism and post-hatch thermotolerance in broiler strains (meat-type chickens). Fewer reports were focused on layer-type chickens and there was no report on amino acid metabolism during TM in layer-type embryos. In this study, we investigated the effects of TM on embryonic development, hepatic amino acid metabolism, and hatching performance in layer-type chickens. Fertilized eggs were incubated under control thermoneutral temperature (CT, 37.6°C) and TM with high temperature (TMH, 39°C, 8 h/day) or low temperature (TML, 20°C, 1 h/day) from embryonic day (ED) 8 to ED 15. The embryonic weight and relative embryonic weight (yolk-free embryonic weight to the initial egg weight) significantly declined in the TML group at ED 13 (P < 0.01) and ED 16 (P < 0.0001), and were significantly increased (P < 0.001) in the TMH group at ED 16, in comparison with the embryos in the CT group. The concentrations of all hepatic free amino acids were significantly increased (P < 0.01) with embryonic development. Interestingly, TMH and TML caused similar effects on hepatic amino acid metabolism, in which most of the essential and non-essential amino acids were significantly declined (P < 0.05) under TM treatments at ED 13 but not affected at ED 16. Until hatching, TML, but not TMH, caused a significant (P < 0.05) delay (31-38 min/day from ED 8) in incubation duration. The hatchability in the TML group was lower than the other two groups, which indicated that 20°C as cold stimulation was not suitable for layer embryos. The body weight, yolk weight, yolk-free body mass, and chick quality were not affected by TM treatments. However, the relative weight of the liver, but not the heart, was significantly reduced (P < 0.05) at hatching by TML treatment. In conclusion, TML, but not TMH, caused to delay in embryogenesis and affected the internal organ of chicks at hatch. Similar changes in amino acid metabolism under TMH and TML indicated that thermal stress induced by both high and low extreme ambient temperatures influences embryonic amino acid metabolism in a similar fashion in layer-type embryos.

Crop Pest Recognition in Real Agricultural Environment Using Convolutional Neural Networks by a Parallel Attention Mechanism.

Crop pests are a major agricultural problem worldwide because the severity and extent of their occurrence threaten crop yield. However, traditional pest image segmentation methods are limited, ineffective and time-consuming, which causes difficulty in their promotion and application. Deep learning methods have become the main methods to address the technical challenges related to pest recognition. We propose an improved deep convolution neural network to better recognize crop pests in a real agricultural environment. The proposed network includes parallel attention mechanism module and residual blocks, and it has significant advantages in terms of accuracy and real-time performance compared with other models. Extensive comparative experiment results show that the proposed model achieves up to 98.17% accuracy for crop pest images. Moreover, the proposed method also achieves a better performance on the other public dataset. This study has the potential to be applied in real-world applications and further motivate research on pest recognition.

Removal of heavy metal ions and anionic dyes from aqueous solutions using amide-functionalized cellulose-based adsorbents.

An efficient, ecofriendly, amide-functionalized cellulose-based porous adsorbent was synthesized by a cross-linking reaction between cellulose filament fibers and bisacrylamide at room temperature. This process is simple, fast and inexpensive, and has significant potential for industrial applications. The prepared material has numerous adsorption sites, resulting in the highly efficient removal of anionic dyes and copper ions from aqueous media. The maximum adsorption capacities of this cellulose-based adsorbent for the dyes Acid Black 1 and Acid Red 18 and for copper ions were 751.8, 417.9, and 51.3 mg g-1, respectively. Regeneration experiments showed that the removal efficiencies for all model pollutants remained above 92 % after five consecutive recycling trials. These results indicate that amide-functionalized cellulose-based adsorbents could possibly be used to treat industrial wastewaters.