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SignificanceThe beef cattle industry plays a pivotal role in the development of China's agricultural economy and the enhancement of people's dietary structure. However, there exists a substantial disparity in feeding management practices and economic efficiency of beef cattle industry compared to developed countries. While the beef cattle industry in China is progressing towards intensive, modern, and large-scale development, it encounters challenges such as labor shortage and rising labor costs that seriously affect its healthy development. The determination of animal physiological indicators plays an important role in monitoring animal welfare and health status. Therefore, leveraging data collected from various sensors as well as technologies like machine learning, data mining, and modeling analysis enables automatic acquisition of meaningful information on beef cattle physiological indicators for intelligent management of beef cattle. In this paper, the intelligent monitoring technology of physiological indicators in beef cattle breeding process and its application value are systematically summarized, and the existing challenges and future prospects of intelligent beef cattle breeding process in China are prospected.ProgressThe methods of obtaining information on beef cattle physiological indicators include contact sensors worn on the body and non-contact sensors based on various image acquisitions. Monitoring the exercise behavior of beef cattle plays a crucial role in disease prevention, reproduction monitoring, and status assessment. The three-axis accelerometer sensor, which tracks the amount of time that beef cattle spend on lying, walking, and standing, is a widely used technique for tracking the movement behavior of beef cattle. Through machine vision analysis, individual recognition of beef cattle and identification of standing, lying down, and straddling movements can also be achieved, with the characteristics of non-contact, stress-free, low cost, and generating high data volume. Body temperature in beef cattle is associated with estrus, calving, and overall health. Sensors for monitoring body temperature include rumen temperature sensors and rectal temperature sensors, but there are issues with their inconvenience. Infrared temperature measurement technology can be utilized to detect beef cattle with abnormal temperatures by monitoring eye and ear root temperatures, although the accuracy of the results may be influenced by environmental temperature and monitoring distance, necessitating calibration. Heart rate and respiratory rate in beef cattle are linked to animal diseases, stress, and pest attacks. Monitoring heart rate can be accomplished through photoelectric volume pulse wave measurement and monitoring changes in arterial blood flow using infrared emitters and receivers. Respiratory rate monitoring can be achieved by identifying different nostril temperatures during inhalation and exhalation using thermal infrared imaging technology. The ruminating behavior of beef cattle is associated with health and feed nutrition. Currently, the primary tools used to detect rumination behavior are pressure sensors and three-axis accelerometer sensors positioned at various head positions. Rumen acidosis is a major disease in the rapid fattening process of beef cattle, however, due to limitations in battery life and electrode usage, real-time pH monitoring sensors placed in the rumen are still not widely utilized. Changes in animal physiology, growth, and health can result in alterations in specific components within body fluids. Therefore, monitoring body fluids or surrounding gases through biosensors can be employed to monitor the physiological status of beef cattle. By processing and analyzing the physiological information of beef cattle, indicators such as estrus, calving, feeding, drinking, health conditions, and stress levels can be monitored. This will contribute to the intelligent development of the beef cattle industry and enhance management efficiency. While there has been some progress made in developing technology for monitoring physiological indicators of beef cattle, there are still some challenges that need to be addressed. Contact sensors consume more energy which affects their lifespan. Various sensors are susceptible to environmental interference which affects measurement accuracy. Additionally, due to a wide variety of beef cattle breeds, it is difficult to establish a model database for monitoring physiological indicators under different feeding conditions, breeding stages, and breeds. Furthermore, the installation cost of various intelligent monitoring devices is relatively high, which also limits its utilization coverage.Conclusion and ProspectsThe application of intelligent monitoring technology for beef cattle physiological indicators is highly significance in enhancing the management level of beef cattle feeding. Intelligent monitoring systems and devices are utilized to acquire physiological behavior data, which are then analyzed using corresponding data models or classified through deep learning techniques to promptly monitor subtle changes in physiological indicators. This enables timely detection of sick, estrus, and calving cattle, facilitating prompt measures by production managers, reducing personnel workload, and improving efficiency. The future development of physiological indicators monitoring technologies in beef cattle primarily focuses on the following three aspects: (1) Enhancing the lifespan of contact sensors by reducing energy consumption, decreasing data transmission frequency, and improving battery life. (2) Integrating and analyzing various monitoring data from multiple perspectives to enhance the accuracy and utility value. (3) Strengthening research on non-contact, high-precision and automated analysis technologies to promote the precise and intelligent development within the beef cattle industry.

Phytophthora strawberries, as a kind of plant pathogenic fungi, can cause strawberry skin and crown rot without safe and effective treatment, which affect the economic benefits of planting strawberries. Therefore, it is urgent to use low-cost diagnostic methods to achieve early prevention. Strawberry plants infected with Phytophthora cactorum would release a unique organic volatile gas, 4-ethylphenol, with a concentration ranging from 1.12 to 22.56 mg/kg, which could be used as a marker gas for rapid diagnosis of the disease. In this study, semiconducting single-walled carbon nanotubes (SWNT) and field effect sensors (FET) were used to prepare semiconductor field effect gas sensors (SWNT/FET) without selectivity. And then the metal porphyrin MnOEP with high sensitivity and selectivity to 4-ethylphenol was immoblized on the SWNT's surface to obtain MnOEP-SWNT/FET. MnOEP-SWNT/FET has the advantages of low cost, low power consumption, small size, high sensitivity and easy integration, which can effectively overcome the shortcomings of gas chromatography-mass spectrometry, high-performance liquid chromatography and other analytical methods. By comparing the sensitivity and selectivity of different sensors, MnOEP-SWNT/FET is very suitable for real-time monitoring of 4-ethylphenol. The key reasons for the high sensitivity and selectivity are: MnOEP is a macromolecular heterocyclic compound formed by four pyrrole rings connected together by methylene and manganese ion(Mn), each pyrrole ring consists of four carbons and one nitrogen, and all nitrogen atoms inside the ring form a central cavity; the coordination metal ions of MnOEP are in an unsaturated state, gas molecules can interact with the central metal ions through van der Waals force and hydrogen bond at the axial position of MnOEP to change their own optical or electrical properties. MnOEP-SWNT/FET was studied by Raman spectrum, UV spectrum and voltammetry. The physical and chemical properties were analyzed and the detection conditions were optimized to improve the gas sensitivity of MnOEP-SWNT/FET to 4-ethylphenol. Under the optimal detection conditions, MnOEP-SWNT/FET has a good linear relationship with 0.25% ~100% saturated vapor of 4-ethylphenol (20 ℃) and the detection limit is 0.15% saturated vapor of 4-ethylphenol. The relative standard error of different concentrations was less than 10%. By measuring the actual samples, it has high detection accuracy for strawberry plants infected with Phytophthora, but it still exists false positive for healthy strawberry.

The production mode of livestock breeding has changed from extensive to intensive, and the production level is improved. However, low labor productivity and labor shortage have seriously restricted the rapid development of China's livestock breeding industry. As a new intelligent agricultural machinery equipment, agricultural robot integrates advanced technologies, such as intelligent monitoring, automatic control, image recognition technology, environmental modeling algorithm, sensors, flexible execution, etc. Using modern information and artificial intelligence technology, developing livestock feeding and pushing robots, realizing digital and intelligent livestock breeding, improving livestock breeding productivity are the main ways to solve the above contradiction. In order to deeply analyze the research status of robot technology in livestock breeding, products and literature were collected worldwide. This paper mainly introduced the research progress of livestock feeding robot from three aspects: Rail feeding robot, self-propelled feeding robot and pushing robot, and analyzed the technical characteristics and practical application of feeding robot.The rail feeding robot runs automatically along the fixed track, identifies the target animal, positions itself, and accurately completes feed delivery through preset programs to achieve accurate feeding of livestock. The self-propelled feeding robot can walk freely in the farm and has automatic navigation and positioning functions. The system takes single chip microcomputer as the control core and realizes automatic walking by sensor and communication module. Compared with the rail feeding robot, the feeding process is more flexible, convenient and technical, which is more conducive to the promotion and application of livestock farms. The pushing robot will automatically push the feed to the feeding area, promote the increase of feed intake of livestock, and effectively reduce the labor demand of the farm. By comparing the feeding robots of developed countries and China from two aspects of technology and application, it is found that China has achieved some innovation in technology, while developted countries do better in product application. The development of livestock robot was prospected. In terms of strategic planning, it is necessary to keep up with the international situation and the trend of technological development, and formulate the agricultural robot development strategic planning in line with China's national conditions. In terms of the development of core technologies, more attention should be paid to the integration of information perception, intelligent sensors and deep learning algorithms to realize human-computer interaction. In terms of future development trends, it is urgent to strengthen innovation, improve the friendliness and intelligence of the robot, and improve the learning ability of the robot. To sum up, intelligent livestock feeding and pushing machine operation has become a cutting-edge technology in the field of intelligent agriculture, which will surely lead to a new round of agricultural production technology reform, promote the transformation and upgrading of China's livestock industry. .

The shortage of feed grain is continually worsening in China, which leads to the transformation of feed grain security into national food security. Therefore, comprehensively integrating the basic data resources of feed nutrition and improving the nutritional value of all available feed resources will be one of the key technical strategies to ensure national food security in China. In this study, based on the description specification and attribute data standard of 16 categories of Chinese feed raw materials, more than 500,000 pieces of data on the types, spatial distribution, chemical composition and nutritional value characteristics of existing feed resources, which were accumulated through previous projects from the sixth Five-Year Plan to the thirteenth Five-Year Plan period, were digitally collected, recorded, categorized and comprehensively analyzed. By using MySQL relational database technology and PHP program, a new generation of feed nutrition big data online platform (http://www.chinafeeddata.org.cn/) was developed and web data sharing service was provided as well. First of all, the online platform provided visual analysis of all warehousing data, which could realize the visual comparison of a single or multiple feed nutrients in various graphic forms such as scatter diagram, histogram, curve line and column chart. By using two-dimensional code technology, all feed nutrition attribute data and feed entity sample traceability data could be shared and downloaded remotely in real-time on mobile phones. Secondly, the online platform also incorporated various regression models for prediction of feed effective nutrient values using readily available feed chemical composition in the datasets, providing dynamic analysis for feed raw material nutrient variation. Finally, based on Geographic Information System technology, the online platform integrated the data of feed chemical composition and major mineral element concentrations with their geographical location information, which was able to provide the distribution query and comparative analysis of the geographic information map of the feed raw material nutrition data at both provincial and national level. Meanwhile, the online platform can also provide a download service of the various datasets, which brought convenience to the comprehensive application of existing feed nutrition data. This research also showed that expanding feed resource data and providing prediction and analysis models of feed effective nutrients could maximize the utilization of the existing feed nutrition data. After embedding online calculation modules of various feed formulation software, this platform would be able to provide a one-stop service and optimize the utilization of the feed nutrition data.

Co‐contamination of mycotoxins produced by fungi in foodstuffs and feeds causes a tremendous health risk to humans and animals. The simultaneous detection of multiple mycotoxins using a cost‐effective and point‐of‐care technology is key to ensuring food safety. In this study, a disposable biosensor with two individual sensing channels is prepared on an affordable cellulose paper substrate and used to simultaneously detect fumatoxin B1 (FB1) and aflatoxin B1 (AFB1). The biosensor pattern is outlined by polydimethylsiloxane (PDMS) and the hydrophobic interface of the sensing channels is created using octadecyltrichlorosilane (OTS). Semiconducting single‐wall carbon nanotubes (s‐SWCNTs) are deposited onto the central zones to serve as the sensing elements and SWCNTs are deposited into the bilateral areas as wire. After functionalization of the s‐SWCNTs, anti‐FB1 and anti‐AFB1 are separately immobilized on the different sensing regions to capture the targeted mycotoxins. Under the optimal conditions, this developed s‐SWCNTs‐based biosensor array achieved a limit of detection (LOD) of 8.23 pg mL−1 for FB1 and 7.48 pg mL−1 for AFB1. As a demonstration, spiked corn samples are measured using this biosensor and recovery rates are not inferior to commercial enzyme‐linked immune sorbent assay (ELISA) kits. Overall, the cost‐effective, highly sensitive, and multiplexed biosensor platform fabricated by this approach shows great potential for detecting multiple mycotoxins. [ABSTRACT FROM AUTHOR]

Intelligent equipment for livestock production is one of the components of intelligent agricultural machinery equipment, and is the focus of technology development in international agricultural equipment industry. This paper reviewed the current situation and development trend of intelligent equipment for livestock production systems nationally and internationally, including electronic feeding stations, animal farming robots, and many supporting intelligent facilities within the animal house. The features and performance characteristics of the equipment were discussed. The development of intelligent equipment for livestock production systems mainly focused on pigs and dairy cows including electronic sow feeding station, lactating sow precision feeding system, electronic cattle feeding station, automatic cattle feeding system, cattle feed pusher and dairy cow milking robot. The development and application of intelligent livestock equipment such as the electronic feeding stations and feeding robots, have significantly increased the production efficiency and saved labor cost in both pig and dairy farms. In addition, it also contributed to improve both of the animal and farmer welfare. However, there is still considerable room to get the application of intelligent livestock equipment improved in practice. For example, the animals have to be trained to get used to the intelligent facilities. On the other hand, the intelligent facilities are also required to identify individual animal or animal organ more accurately in order to further increase the production efficiency. Therefore, the key features in the further development of intelligent livestock equipment would be smarter, more convenient, more reliable, and more economical. At the meantime, it should be a highly integrated and coordinated intelligent system including intelligent facilities, well trained staff, good animal welfare, and comfortable environment. Therefore, the industrial application of the intelligent livestock equipment should be integrated with the local farming practice and fitted with the layout of animal houses in order to increase the efficiency of the equipment, and consequently, to improve animal welfare. The systematical combination of intelligent facilities and animal physiology, animal growth, and animal behavior could contribute to the dynamic interactions between the equipment and animal. Finally, it was concluded that the development of intelligent equipment should be coordinated with the theory of animal production, the function of animal products and the innovation of farming practice. And it also should be continuously updated to promote the transformation and upgrading of animal husbandry industry.

As large-scale beef cattle facilities become more and more common in China, sophisticated and even intelligent management of cattle has gained prominence. In the meantime, the Internet of Things, big data, artificial intelligence (AI) and even large models are developing at a rapid pace and are constantly permeating in all industries, making the intelligent management, including traditional breeding, possible. In this study, Yangxin Yi Liyuan 5G digital farm was used as the research object, which integrated application of intelligent electronic ear tags and intelligent collars, as well as a variety of environmental sensors for temperature and humidity, ammonia, carbon dioxide, wind direction and speed, light and air quality (H2S, PM2.5, PM10, TSP). Simultaneously, a comprehensive dynamic perception of individual physiological indicators, such as the degree of exercise and rumination, was utilized to determine the estrus period of breeding cattle and forecast the ideal breeding period, and was employed to ascertain the estrus period of breeding cattle, forecast the ideal mating period, and determine whether the feed adjustment was necessary by monitoring variations in rumination time. Comprehensively monitoring the environmental indicators, such as temperature, humidity and air quality, is necessary to accurately manage and ventilate the cattle house. The pertinent data was analyzed using the MY SQL database technology and DELPHI language technology. In order to create a digital control platform for the breeding environment, health status, epidemic prevention, and feed management of beef cattle, this research built a digital control platform that integrated automatic collection of key data in breeding links, automatic conversion and calculation of data, wired and wireless transmission of data, remote storage and processing control of data, and so on. This study also shown that the more the system was used and improved, the more data was collected and extended in both size and type during the breeding process, and the more fundamental and derived data could be mined and appreciated. This paved way for the eventual development of a large-scale model for breeding beef cattle in the future. [ABSTRACT FROM AUTHOR]