Realizing the construction of intelligent farming by using advanced information technology, thus improving the living welfare of dairy cows and the economic benefits of dairy farms has become an important goal and task in dairy farming research field. Computer vision technology has the advantages of non-contact, stress-free, low cost and high throughput, and has a broad application prospect in animal production. On the basis of describing the importance of computer vision technology in the development of intelligent farming industry, this paper introduced the cutting-edge technology of cow physiological parameters and disease diagnosis based on computer vision, including cow temperature monitoring, body size monitoring, weight measurement, mastitis detection and lameness detection. The introduction coverd the development process of these studies, the current mainstream techniques, and discussed the problems and challenges in the research and application of related technology, aiming at the problem that the current computer vision-based detection methods are susceptible to individual difference and environmental changes. Combined with the development status of farming industry, suggestions on how to improve the universality of computer vision technology in intelligent farming industry, how to improve the accuracy of monitoring cows' physiological parameters and disease diagnosis, and how to reduce the influence of environment on the system were put forward. Future research work should focus on research and developmentof algorithm, make full use of computer vision technology continuous detection and the advantage of large amount of data, to ensure the accuracy of the detection, and improve the function of the system integration and data utilization, expand the computer vision system function. Under the premise that does not affect the ability of the system, to improve the study on the number of function integration and system function and reduce equipment costs.
Pig welfare is closely related to the economical production of pig farms. With regard to pig welfare assessment, pig sounds are significant indicators, which can reflect the quality of the barn environment, the physical condition and the health of pigs. Therefore, pig sound analysis is of high priority and necessary. In this review, the relationship between pig sound and welfare was analyzed. Three kinds of pig sounds are closely related to pig welfare, including coughs, screams, and grunts. Subsequently, both wearable and non-contact sensors were briefly described in two aspects of advantages and disadvantages. Based on the advantages and feasibility of microphone sensors in contactless way, the existing techniques for processing pig sounds were elaborated and evaluated for further in-depth research from three aspects: sound recording and labeling, feature extraction, and sound classification. Finally, the challenges and opportunities of pig sound research were discussed for the ultimate purpose of precision livestock farming (PLF) in four ways: concerning sound monitoring technologies, individual pig welfare monitoring, commercial applications and pig farmers. In summary, it was found that most of the current researches on pig sound recognition tasks focused on the selection of classifiers and algorithm improvement, while fewer research was conducted on sound labeling and feature extraction. Meanwhile, pig sound recognition faces some challenging problems, involving the difficulty in obtaining the audio data from different pig growth stages and verifying the developed algorithms in a variety of pig farms. Overall, it is suggested that technologies involved in the automatic identification process should be explored in depth. In the future, strengthen cooperation among cross-disciplinary experts to promote the development and application of PLF is also nessary.
The motion behavior of dairy cows contains much of health information. The application of information and intelligent technology will help farms grasp the health status of dairy cows in time and improve breeding efficiency. In this paper, the development trend of intelligent morning technology of cow's motion behavior was mainly analyzed. Firstly, on the basis of expounding the significance of monitoring the basic motion (lying, walking, standing), oestrus, breathing, rumination and limping of dairy cows, the necessity of behavior monitoring of dairy cows was introduced. Secondly, the current research status was summarized from contact monitoring methods and non-contact monitoring methods in chronological order. The principle and achievements of related research were introduced in detail and classified. It is found that the current contact monitoring methods mainly rely on acceleration sensors, pedometers and pressure sensors, while the non-contact monitoring methods mainly rely on video images, including traditional video image analysis and video image analysis based on deep learning. Then, the development status of cow behavior monitoring industry was analyzed, and the main businesses and mainstream products of representative livestock farm automation equipment suppliers were listed. Industry giants, such as Afimilk and DeLaval, as well as their products such as intelligent collar (AfiCollar), pedometer (AfiActll Tag) and automatic milking equipment (VMS™ V300) were introduced. After that, the problems and challenges of current contact and non-contact monitoring methods of dairy cow motion behavior were put forward. The current intelligent monitoring methods of dairy cows' motion behavior are mainly wearable devices, but they have some disadvantages, such as bring stress to dairy cows and are difficult to install and maintain. Although the non-contact monitoring methods based on video image analysis technology does not bring stress to dairy cows and is low cost, the relevant research is still in its infancy, and there is still a certain distance from commercial use. Finally, the future development directions of relevant key technologies were prospected, including miniaturization and integration of wearable monitoring equipment, improving the robustness of computer vision technology, multi-target monitoring with limited equipment and promoting technology industrialization.
The gait phase of dairy cows is an important indicator to reflect the severity of lameness. IThe accuracy of available gait segmentation methods was not enough for lameness detection. In this study, a gait phase recognition method based on Gaussian mixture model (GMM) and hidden Markov model (HMM) was proposed and tested. Firstly, wearable inertial sensors LPMS-B2 were used to collect the acceleration and angular velocity signals of cow hind limbs. In order to remove the noise of the system and restore the real dynamic data, Kalman filter was used for data preprocessing. The first-order difference of the angular velocity of the coronal axis was selected as the eigenvalue. Secondly, to analyze the long-term continuous recorded gait sequences of dairy cows, the processed data was clustered by GMM in the unsupervised way. The clustering results were taken as the input of the HMM, and the gait phase recognition of dairy cows was realized by decoding the observed data. Finally, the cow gait was segmented into 3 phases, including the stationary phase, standing phase and swing phase. At the same time, gait segmentation was achieved according to the standing phase and swing phase. The accuracy, recall rate and F1 of the stationary phase were 89.28%, 90.95% and 90.91%, respectively. The accuracy, recall rate and F1 of the standing phase recognition in continuous gait were 91.55%, 86.71% and 89.06%, respectively. The accuracy, recall rate and F1 of the swing phase recognition in continuous gait were 86.67%, 91.51% and 89.03%, respectively. The accuracy of cow gait segmentation was 91.67%, which was 4.23% and 1.1 % higher than that of the event-based peak detection method and dynamic time warping algorithm, respectively. The experimental results showed that the proposed method could overcome the influence of the cow's walking speed on gait phase recognition results, and recognize the gait phase accurately. This experiment provides a new method for the adaptive recognition of the cow gait phase in unconstrained environments. The degree of lameness of dairy cows can be judged by the gait features.
Non-contact measurement based on the point cloud acquisition technology is able to alleviate the stress responses among beef cattle while collecting core body dimension data, but the current 3D data collection for beef cattle is usually time-consuming and easily influenced by the environment, which is in fact inapplicable to the actual breeding environment. In order to overcome the difficulty in obtaining the complete beef cattle point clouds, a non-contact phenotype data acquisition equipment was developed with a 3D reconstruction function, which can provide a large amount of standardized 3D quantitative phenotype data for beef cattle breeding and fattening process. The system is made up of a Kinect DK depth camera, an infrared grating trigger, and an Radio Frequency Identification (RFID) trigger, which enables the multi-angle instantaneous acquisition of beef cattle point clouds when the beef cattle pass through the walkway. The point cloud processing algorithm was developed based on the C++ platform and Point Cloud Library (PCL), and 3D reconstruction of beef cattle point clouds was achieved through spatial and outlier point filtering, Random Sample Consensus (RANSAC) shape fitting, point cloud thinning, and perceptual box filtering based on the dimensionality reduction density clustering to effectively filter out the interference, such as noises from the railings close to the beef cattle, without destroying the integrity of the point clouds. In the present work, a total of 124 sets of point clouds were successfully collected from 20 beef cattles on the actual farm using this system, and the target extraction experiments were completed. Notably, the beef cattle passed through the walkway in a natural state without any intervention during the whole data collection process. The experimental results showed that the acquisition success rate of this device was 91.89%. The coordinate system of the collected point cloud was consistent with the real situation and the body dimension reconstruction error was 0.6%. This device can realize the automatic acquisition and 3D reconstruction of beef cattle point cloud data from multiple angles without human intervention, and can automatically extract the target beef cattle point clouds from a complex environment. The point cloud data collected by this system help to restore the body size and shape of beef cattle, thereby provide solid support for the measurement of core parameters such as body height, body width, body oblique length, chest circumference, abdominal circumference, and body weight.
Identifying of yak is indispensable for individual documentation, behavior monitoring, precise feeding, disease prevention and control, food traceability, and individualized breeding. Aiming at the application requirements of animal individual identification technology in intelligent informatization animal breeding platforms, a yak face recognition algorithm based on transfer learning and multiscale feature fusion, i.e., transfer learning-multiscale feature fusion-VGG(T-M-VGG) was proposed. The sample data set of yak facial images was produced by a camera named GoPro HERO8 BLACK. Then, a part of dataset was increased by the data enhancement ways that involved rotating, adjusting the brightness and adding noise to improve the robustness and accuracy of model. T-M-VGG, a kind of convolutional neural network based on pre-trained visual geometry group network and transfer learning was input with normalized dataset samples. The feature map of Block3, Block4 and Block5 were considered as F3, F4 and F5, respectively. What's more, F3 and F5 were taken by the structure that composed of three parallel dilated convolutions, the dilation rate were one, two and three, respectively, to dilate the receptive filed which was the map size of feature map. Further, the multiscale feature maps were fused by the improved feature pyramid which was in the shape of stacked hourglass structure. Finally, the fully connected layer was replaced by the global average pooling to classify and reduce a large number of parameters. To verify the effectiveness of the proposed model, a comparative experiment was conducted. The experimental results showed that recognition accuracy rate in 38,800 data sets of 194 yaks reached 96.01%, but the storage size was 70.75 MB. Twelve images representing different yak categories from dataset were chosen randomly for occlusion test. The origin images were masked with different shape of occlusions. The accuracy of identifying yak individuals was 83.33% in the occlusion test, which showed that the model had mainly learned facial features. The proposed algorithm could provide a reference for research of yak face recognition and would be the foundation for the establishment of smart management platform.
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. .
Focusing on the low level of management and informatization and intelligence of the beef cattle industry in China, a big data platform for commercial beef cattle breeding, referring to the experience of international advanced beef cattle breeding countries, was proposed in this research. The functions of the platform includes integrating germplasm resources of beef cattle, automatic collecting of key beef cattle breeding traits, full-service support for the beef cattle breeding process, formation of big data analysis and decision-making system for beef cattle germplasm resources, and joint breeding innovation model. Aiming at the backward storage and sharing methods of beef cattle breeding data and incomplete information records in China, an information resource integration platform and an information database for beef cattle germplasm were established. Due to the vagueness and subjectivity of the breeding performance evaluation standard, a scientific online evaluation technology of beef cattle breeding traits and a non-contact automatic acquisition and intelligent calculation method were proposed. Considering the lack of scientific and systematic breeding planning and guidance for farmers in China, a full-service support system for beef cattle breeding and nanny-style breeding guidance during beef cattle breeding was developed. And an interactive progressive decision-making method for beef cattle breeding to solve the lack of data accumulation of beef cattle germplasm was proposed. The main body of breeding and farming enterprises was not closely integrated, according to that, the innovative breeding model of regional integration was explored. The idea of commercial beef cattle breeding big data software platform and the technological and model innovation content were also introduced. The technology innovations included the deep mining of germplasm resources data and improved breed management pedigree, the automatic acquisition and evaluation technology of non-contact breeding traits, the fusion of multi-source heterogeneous information to provide intelligent decision support. The future goal is to form a sustainable information solution for China's beef cattle breeding industry and improve the overall level of China's beef cattle breeding 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.
