An enhancement algorithm for head characteristics of caged chickens detection based on cyclic consistent migration neural network.

The enclosed multistory poultry housing is a type of poultry enclosure widely used in industrial caged chicken breeding. Accurate identification and detection of the comb and eyes of caged chickens in poultry farms using this type of enclosure can enhance managers' understanding of the health of caged chickens. However, the accuracy of image detection of caged chickens will be affected by the enclosure's entrance, which will reduce the precision. Therefore, this paper proposes a cage-gate removal algorithm based on big data and deep learning Cyclic Consistent Migration Neural Network (CCMNN). The method achieves automatic elimination and restoration of some key information in the image through the CCMNN network. The Structural Similarity Index Measure (SSIM) between the recovered and original images on the test set is 91.14%. Peak signal-to-noise ratio (PSNR) is 25.34dB. To verify the practicability of the proposed method, the performance of the target detection algorithm is analyzed both before and after applying the CCMNN network in detecting the combs and eyes of caged chickens. Different YOLOv8 detection algorithms, including YOLOv8s, YOLOv8n, YOLOv8m, and YOLOv8x, were used to verify the algorithm proposed in this paper. The experimental results demonstrate that compared to images without CCMNN processing, the precision of comb detection of caged chickens is improved by 11, 11.3, 12.8, and 10.2%. Similarly, the precision of eye detection for caged chickens is improved by 2.4, 10.2, 6.8, and 9%. Therefore, more complete outline images of caged chickens can be obtained using this algorithm and the precision in detecting the comb and eyes of caged chickens can be enhanced. These advancements in the algorithm offer valuable insights for future poultry researchers aiming to deploy enhanced detection equipment, thereby contributing to the accurate assessment of poultry production and farm conditions.

Diversity and correlation analysis of different root exudates on the regulation of microbial structure and function in soil planted with Panax notoginseng.

Introduction: Specific interactions between root exudates and soil microorganisms has been proposed as one of the reasons accounting for the continuous cropping obstacle (CCO) of Panax notoginseng. However, rotation of other crops on soils planted with P. notoginseng (SPP) did not show CCO, suggesting that root exudates of different crops differentially regulate soil microorganisms in SPP. Methods: Here, we investigated the microbial community structure and specific interaction mechanisms of the root exudates of the four plant species, P. notoginseng (Pn), Zea mays (Zm), Nicotiana tabacum (Nt) and Perilla frutescens (Pf), in SPP by static soil culture experiment. Results: The results showed that the chemical diversity of root exudates varied significantly among the four plant species. Pn had the highest number of unique root exudates, followed by Nt, Zm and Pf. Terpenoids, flavonoids, alkaloids and phenolic acids were the most abundant differentially accumulated metabolites (DAMs) in Pn, Nt, Zm and Pf, respectively. However, lipids were the most abundant common DAMs among Zm Nt and Pf. Pn root exudates decreased the relative abundance of bacteria, but increased that of fungi. While specific DAMs in Pn enriched Phenylobacterium_zucineum, Sphingobium_yanoikuyae, Ophiostoma_ulmi and functional pathways of Nucleotide excision repair, Streptomycin biosynthesis, Cell cycle-Caulobacter and Glycolysis/Gluconeogenesis, it inhibited Paraburkholderia _caledonica and Ralstonia_pickettii. However, common DAMs in Zm, Nt and Pf had opposite effects. Moreover, common DAMs in Zm, Nt and Pf enriched Ralstonia_pseudosolanacearum and functional pathway of Xylene degradation; unique DAMs in Zm enriched Talaromyces_purcureogeneus, while inhibiting Fusarium_tricinctum and functional pathways of Nucleotide excision repair and Alanine, aspartate and glutamate metabolism; unique DAMs in Pf enriched Synchytrium_taraxaci. Discussion: The core strains identified that interact with different root exudates will provide key clues for regulation of soil microorganisms in P. notoginseng cultivation to alleviate CCO.

Automatic Detection Method of Dairy Cow Feeding Behaviour Based on YOLO Improved Model and Edge Computing.

The feeding behaviour of cows is an essential sign of their health in dairy farming. For the impression of cow health status, precise and quick assessment of cow feeding behaviour is critical. This research presents a method for monitoring dairy cow feeding behaviour utilizing edge computing and deep learning algorithms based on the characteristics of dairy cow feeding behaviour. Images of cow feeding behaviour were captured and processed in real time using an edge computing device. A DenseResNet-You Only Look Once (DRN-YOLO) deep learning method was presented to address the difficulties of existing cow feeding behaviour detection algorithms' low accuracy and sensitivity to the open farm environment. The deep learning and feature extraction enhancement of the model was improved by replacing the CSPDarknet backbone network with the self-designed DRNet backbone network based on the YOLOv4 algorithm using multiple feature scales and the Spatial Pyramid Pooling (SPP) structure to enrich the scale semantic feature interactions, finally achieving the recognition of cow feeding behaviour in the farm feeding environment. The experimental results showed that DRN-YOLO improved the accuracy, recall, and mAP by 1.70%, 1.82%, and 0.97%, respectively, compared to YOLOv4. The research results can effectively solve the problems of low recognition accuracy and insufficient feature extraction in the analysis of dairy cow feeding behaviour by traditional methods in complex breeding environments, and at the same time provide an important reference for the realization of intelligent animal husbandry and precision breeding.

WC-High Entropy Alloy Reinforced Long Life Self-Grinding Silage Knife Prepared by Laser Cladding.

The working environment of agricultural knives is bad, which makes the knives wear out easily. A wear resistant layer of AlCoCrFeNi high entropy alloy (HEA) reinforced by tungsten carbide (WC) was prepared by laser cladding on one side of the cutting edge of a 65 Mn silage knife. Both the effects of WC addition on the microstructure and mechanical properties of AlCoCrFeNi (WC)x (x = 0, 0.1, 0.2 and 0.3 in mass percentage) alloys were investigated. All experimental alloys displayed a crystalline structure of simple body centered cubic (BCC). The hardness of the cladding layer increases with the increase of WC content, and the hardness value enhances from 740 HV0.2 to 1060 HV0.2. A self-grinding edge was formed during working for the cladded knives. The cutting quality can be improved and the service life of agricultural knives can be increased meanwhile. The weight loss rate of untreated knives was about 2.64 times that of the cladded knives after a 76 h field experiment.

Leveraging SOLOv2 model to detect heat stress of poultry in complex environments.

Heat stress is one of the most important environmental stressors facing poultry production. The presence of heat stress will reduce the antioxidant capacity and immunity of poultry, thereby seriously affecting the health and performance of poultry. The paper proposes an improved FPN-DenseNet-SOLO model for poultry heat stress state detection. The model uses Efficient Channel Attention (ECA) and DropBlock regularization to optimize the DenseNet-169 network to enhance the extraction of poultry heat stress features and suppress the extraction of invalid background features. The model takes the SOLOv2 model as the main frame, and uses the optimized DenseNet-169 as the backbone network to integrate the Feature Pyramid Network to detect and segment instances on the semantic branch and mask branch. In the validation phase, the performance of FPN-DenseNet-SOLO was tested with a test set consisting of 12,740 images of poultry heat stress and normal state, and it was compared with commonly used object detection models (Mask R CNN, Faster RCNN and SOLOv2 model). The results showed that when the DenseNet-169 network lacked the ECA module and the DropBlock regularization module, the original model recognition accuracy was 0.884; when the ECA module was introduced, the model's recognition accuracy improved to 0.919. Not only that, the recall, AP0.5, AP0.75 and mean average precision of the FPN-DenseNet-SOLO model on the test set were all higher than other networks. The recall is 0.954, which is 15, 8.8, and 4.2% higher than the recall of Mask R CNN, Faster R CNN and SOLOv2, respectively. Therefore, the study can achieve accurate segmentation of poultry under normal and heat stress conditions, and provide technical support for the precise breeding of poultry.

Molecular orientation and stability of poly(3-hexylthiophene) nanogratings affected by the fabricated solvent vapor.

During the nanoimprinting lithography (NIL) process, the role of solvent vapor in fabricating the pattern structure and inducing the molecular alignment of nanoimprinted polymer film has been attracting significant attention. We demonstrate here that the molecular orientation and thermal stability of poly(3-hexylthiophene) (P3HT) nanograting film can be affected obviously by the fabricated solvent vapor. A solvent-vapor nanoimprinting lithography (SV-NIL) technique based on a polydimethylsiloxane (PDMS) template is employed to fabricate a P3HT nanograting structure film successfully and solvent vapor is offered by chlorobenzene, chloroform and carbon disulphide, respectively. The molecular orientation of the polymer film is carefully characterized by grazing incidence wide angle X-ray diffraction (GIWAXD) measurements to investigate the effect of various solvent vapors on the molecular orientation of the P3HT nanograting film. For the P3HT nanograting film fabricated by chloroform and chlorobenzene solvent, the edge-on molecular orientation of the typical form II crystallographic structure is induced. However, this indicates that there are both the face-on molecular orientations of the form II and form I crystallographic conformation present for the P3HT nanograting film fabricated by carbon disulphide solvent. Therefore, the fabricated solvent vapor plays a significant role in determining the formation of the molecular orientation of the polymer nanostructure. Then, the role of thermal annealing in the stability of the molecular orientation was investigated for the P3HT nanograting film after a fixed temperature. As for the annealed nanograting film fabricated by chlorobenzene and chloroform solvent vapor, a single edge-on molecular orientation mode of the form I crystallographic structure has been obtained. However, for the annealed nanograting film fabricated by the carbon disulphide solvent, the edge-on and face-on molecular orientations of the form I crystallographic structure are still retained. This indicates that the stability of the form II crystallographic conformation is mainly dependent on the thermal annealing process. Therefore, after the annealing process, the final determining of the molecular alignment and crystallographic conformation depends significantly on the orientation type of the nanograting film before the annealing history, and it can be further inferred that the molecular orientation of the annealed polymer film is still affected by the fabricated solvent vapor significantly. Thus this will provide new understanding and guidance for the research of the topographical structure and molecular alignment of conjugated polymers.

Prevention of enzymatic browning of Chinese yam (Dioscorea spp.) using electrolyzed oxidizing water.

In this study, the effects of electrolyzed oxidizing water (EOW) on the prevention of enzymatic browning of fresh-cut "Jiu Jinhuang" Chinese yam were investigated. The yams were immersed in the inhibitors for 25 min at 20 °C. Compared with the tap water (TW) treatment, the chromatic attributes were significantly different after 72 h of storage (P < 0.05). The activities of polyphenol oxidase (PPO, EC 1.10.3.1), peroxidase (POD, EC 1.11.1.7), and L -phenylalanine ammonia lyase (PAL, EC 4.3.1.5) were inhibited when measured at 24 h. The contents of phenolic acids, including gallic and chlorogenic acid, in the group treated with the slightly acidic electrolyzed water (SAEW) were higher than those treated with TW and neutral electrolyzed water (NEW). The group treated with NEW had the highest total phenol content (P < 0.05, at 24 h), while the group treated with SAEW had the highest flavonoid content (P < 0.05) during storage. Without being treated with inhibitors, the Km and Vmax values of yam PPO were 0.0044 mol/L and 0.02627 U/min, respectively, and the Ki of samples treated with SAEW and citric acid (CA) were 15.6607 and 2.3969 µmol/L, respectively. These results indicate that EOW is beneficial as a browning inhibitor.