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Waterfowl farming in China is developing rapidly in the direction of large-scale, standardization and intelligence. The research and application of intelligent farming equipment and information technology is the key to promote the healthy and sustainable development of waterfowl farming, which is important to improve the output efficiency of waterfowl farming, reduce the reliance on labor in the production process, fit the development concept of green and environmental protection and achieve high-quality transformational development. In this paper, the latest research and inventions of intelligent waterfowl equipment, waterfowl shed environment intelligent control technology and intelligent waterfowl feeding, drinking water, dosing and disinfection and automatic manure treatment equipment were introduced. At present, compared to pigs, chickens and cattle, the intelligent equipment of waterfowl are still relatively backward. Most waterfowl houses are equipped with chicken equipment directly, lacking improvements for waterfowl. Moreover, the linkage between the equipment is poor and not integrated with the breeding mode and shed structure of waterfowl, resulting in low utilization. Therefore, there is a need to develop and improve equipment for the physiological growth characteristics of waterfowl from the perspective of their breeding welfare. In addition, the latest research advances in the application of real-time production information collection and intelligent management technologies were present. The information collection technologies included visual imaging technology, sound capture systems, and wearable sensors were present. Since the researches of ducks and geese is few, the research of poultry field, which can provide a reference for the waterfowl were also summarized. The research of information perception and processing of waterfowl is currently in its initial stage. Information collection techniques need to be further tailored to the physiological growth characteristics of waterfowl, and better deep learning models need to be established. The waterfowl management platform, taking the intelligent management platform developed by South China Agricultural University as an example were also described. Finally, the intelligent application of the waterfowl industry was pointed out, and the future trends of intelligent farming with the development of mechanized and intelligent equipment for waterfowl in China to improve the recommendations were analyzed. The current waterfowl farming is in urgent need of intelligent equipment reform and upgrading of the industry for support. In the future, intelligent equipment for waterfowl, information perception methods and control platforms are in urgent to be developed. When upgrading the industry, it is necessary to develop a development strategy that fits the current waterfowl farming model in China.

Simple Summary: Simple Summary: Pig tracking provides strong support for refined management in pig farms and can assist in realizing customized, automated, and intellectualized management. However, there are many complex factors in actual production that result in pigs not being tracked consistently over time. To address this issue, we proposed a long-term video tracking method for group-housed pigs based on improved StrongSORT. The experimental results proved that our study demonstrates high practicality, catering to the needs of practical production. It improved the efficiency of farming personnel and lays the foundation for the automated monitoring of pigs. Additionally, it is worth mentioning that we have also released a dataset specifically designed for long-term pig tracking in this paper. Pig tracking provides strong support for refined management in pig farms. However, long and continuous multi-pig tracking is still extremely challenging due to occlusion, distortion, and motion blurring in real farming scenarios. This study proposes a long-term video tracking method for group-housed pigs based on improved StrongSORT, which can significantly improve the performance of pig tracking in production scenarios. In addition, this research constructs a 24 h pig tracking video dataset, providing a basis for exploring the effectiveness of long-term tracking algorithms. For object detection, a lightweight pig detection network, YOLO v7-tiny_Pig, improved based on YOLO v7-tiny, is proposed to reduce model parameters and improve detection speed. To address the target association problem, the trajectory management method of StrongSORT is optimized according to the characteristics of the pig tracking task to reduce the tracking identity (ID) switching and improve the stability of the algorithm. The experimental results show that YOLO v7-tiny_Pig ensures detection applicability while reducing parameters by 36.7% compared to YOLO v7-tiny and achieving an average video detection speed of 435 frames per second. In terms of pig tracking, Higher-Order Tracking Accuracy (HOTA), Multi-Object Tracking Accuracy (MOTP), and Identification F1 (IDF1) scores reach 83.16%, 97.6%, and 91.42%, respectively. Compared with the original StrongSORT algorithm, HOTA and IDF1 are improved by 6.19% and 10.89%, respectively, and Identity Switch (IDSW) is reduced by 69%. Our algorithm can achieve the continuous tracking of pigs in real scenarios for up to 24 h. This method provides technical support for non-contact pig automatic monitoring. [ABSTRACT FROM AUTHOR]

The silkworm, a crucial model organism of the Lepidoptera, offers an excellent platform for investigating the molecular mechanisms underlying the innate immune response of insects toward pathogens. Over the years, researchers worldwide have identified numerous immune-related genes in silkworms. However, these identified silkworm immune genes are not well classified and not well known to the scientific community. With the availability of the latest genome data of silkworms and the extensive research on silkworm immunity, it has become imperative to systematically categorize the immune genes of silkworms with different database IDs. In this study, we present a meticulous organization of prevalent immune-related genes in the domestic silkworm, using the SilkDB 3.0 database as a reliable source for updated gene information. Furthermore, utilizing the available data, we classify the collected immune genes into distinct categories: pattern recognition receptors, classical immune pathways, effector genes and others. In-depth data analysis has enabled us to predict some potential antiviral genes. Subsequently, we performed antiviral experiments on selected genes, exploring their impact on Bombyx mori nucleopolyhedrovirus replication. The outcomes of this research furnish novel insights into the immune genes of the silkworm, consequently fostering advancements in the field of silkworm immunity research by establishing a comprehensive classification and functional understanding of immune-related genes in the silkworm. This study contributes to the broader understanding of insect immune responses and opens up new avenues for future investigations in the domain of host-pathogen interactions., (© 2024 Institute of Zoology, Chinese Academy of Sciences.)