Journal articles: 'Animal automatic identification'

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Hu, Xu Ling. "Research on Radio Frequency Identification Technology." Applied Mechanics and Materials 299 (February 2013): 152–55. http://dx.doi.org/10.4028/www.scientific.net/amm.299.152.

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Radio frequency identification (RFID) is an automatic identification technology, is characterized by its greatest non-contact identification. In recent years, RFID technology is widely used in transportation management, logistics management, production automation, security access checking, warehousing management, security management, animal management, and other fields. Apply RFID technology to manage applications for research is important.

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., G. N. Murugananthan. "AUTOMATIC IDENTIFICATION OF ANIMAL USING VISUAL AND MOTION SALIENCY." International Journal of Research in Engineering and Technology 03, no. 04 (April 25, 2014): 64–68. http://dx.doi.org/10.15623/ijret.2014.0304012.

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Gouws, J. "Tegnologie vir ten volle geoutomatiseerde melking van koeie." Suid-Afrikaanse Tydskrif vir Natuurwetenskap en Tegnologie 13, no. 4 (July 10, 1994): 120–24. http://dx.doi.org/10.4102/satnt.v13i4.593.

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Since dairy farming is a very labour intensive, seven-days-per-week activity, increasing emphasis is being placed on the use of advanced technology in dairying throughout the world. Dairy mechanisation has been well established for many years, whereas dairy automation has only started to gain momentum fairly recently. An important milestone was the introduction of systems for automatic animal identification in the 1970’s. That paved the way for all further dairy automation activities. An analysis of the current status of the fully automated milking of cows shows that the automated attachment of a milking machine’s teat cups to a cow ’s teats is the most important task in dairying that remains to be automated.

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Slader, R. W., and A. M. S. Gregory. "Electronic live animal and carcass identification systems." Proceedings of the British Society of Animal Production (1972) 1990 (March 1990): 102. http://dx.doi.org/10.1017/s0308229600018833.

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Unique and secure identification of livestock from birth through to the carcass in the abattoir will become a key feature in many areas of livestock production: disease eradication; quality assurance schemes; pedigree breeding; and producer feedback from abattoirs. There have been many attempts at adapting traditional methods of animal identification to provide a measure of automatic recognition. None has been as successful as the application of radio frequency transponders which transmit a unique code when triggered by a suitable reader.

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Stowell, Dan, Tereza Petrusková, Martin Šálek, and Pavel Linhart. "Automatic acoustic identification of individuals in multiple species: improving identification across recording conditions." Journal of The Royal Society Interface 16, no. 153 (April 10, 2019): 20180940. http://dx.doi.org/10.1098/rsif.2018.0940.

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Many animals emit vocal sounds which, independently from the sounds’ function, contain some individually distinctive signature. Thus the automatic recognition of individuals by sound is a potentially powerful tool for zoology and ecology research and practical monitoring. Here, we present a general automatic identification method that can work across multiple animal species with various levels of complexity in their communication systems. We further introduce new analysis techniques based on dataset manipulations that can evaluate the robustness and generality of a classifier. By using these techniques, we confirmed the presence of experimental confounds in situations resembling those from past studies. We introduce data manipulations that can reduce the impact of these confounds, compatible with any classifier. We suggest that assessment of confounds should become a standard part of future studies to ensure they do not report over-optimistic results. We provide annotated recordings used for analyses along with this study and we call for dataset sharing to be a common practice to enhance the development of methods and comparisons of results.

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Zhou, Meilun, Jared A. Elmore, Sathishkumar Samiappan, Kristine O. Evans, Morgan B. Pfeiffer, Bradley F. Blackwell, and Raymond B. Iglay. "Improving Animal Monitoring Using Small Unmanned Aircraft Systems (sUAS) and Deep Learning Networks." Sensors 21, no. 17 (August 24, 2021): 5697. http://dx.doi.org/10.3390/s21175697.

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In recent years, small unmanned aircraft systems (sUAS) have been used widely to monitor animals because of their customizability, ease of operating, ability to access difficult to navigate places, and potential to minimize disturbance to animals. Automatic identification and classification of animals through images acquired using a sUAS may solve critical problems such as monitoring large areas with high vehicle traffic for animals to prevent collisions, such as animal-aircraft collisions on airports. In this research we demonstrate automated identification of four animal species using deep learning animal classification models trained on sUAS collected images. We used a sUAS mounted with visible spectrum cameras to capture 1288 images of four different animal species: cattle (Bos taurus), horses (Equus caballus), Canada Geese (Branta canadensis), and white-tailed deer (Odocoileus virginianus). We chose these animals because they were readily accessible and white-tailed deer and Canada Geese are considered aviation hazards, as well as being easily identifiable within aerial imagery. A four-class classification problem involving these species was developed from the acquired data using deep learning neural networks. We studied the performance of two deep neural network models, convolutional neural networks (CNN) and deep residual networks (ResNet). Results indicate that the ResNet model with 18 layers, ResNet 18, may be an effective algorithm at classifying between animals while using a relatively small number of training samples. The best ResNet architecture produced a 99.18% overall accuracy (OA) in animal identification and a Kappa statistic of 0.98. The highest OA and Kappa produced by CNN were 84.55% and 0.79 respectively. These findings suggest that ResNet is effective at distinguishing among the four species tested and shows promise for classifying larger datasets of more diverse animals.

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Dela Rue, B. T., C. R. Eastwood, J. P. Edwards, and S. Cuthbert. "New Zealand dairy farmers preference investments in automation technology over decision-support technology." Animal Production Science 60, no. 1 (2020): 133. http://dx.doi.org/10.1071/an18566.

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Dairy farmers are adopting precision technologies to assist with milking and managing their cows due to increased herd sizes and a desire to improve labour efficiency, productivity and sustainability. In the present study, we evaluated the adoption of technologies installed at or near the dairy, and milking practices, on New Zealand dairy farms. These data quantify current use of technology for milking and labour efficiency, and decision-making, and provide insight into future technology adoption. A telephone survey of 500 farmers, randomly selected from a database of New Zealand dairy farms, was conducted in 2018. Adoption for all farms is indicated for six automation technologies, including automatic cup removers (39%), automatic drafting (24%), automatic teat spraying (29%), computer-controlled in-shed feeding (29%), automatic plant wash (18%) and automatic yard wash systems (27%). Five data-capture technologies also included in the survey were electronic milk meters (8%), automatic animal weighing (7%), in-line mastitis detection (7%), automatic heat detection (3%) and electronic animal-identification readers (23%). Analysis by dairy type indicated an adoption level for the automation technologies in rotary dairies of 36–77%, and 7–49% for data-capture technologies, with 10% having none of these 11 technologies installed. This compares with herringbone dairies at 4–21% and 2–11% for automation and data-capture technologies respectively, with 56% having none of these technologies. Rotary dairies, with a combination of automatic cup removers, automatic teat spraying, and automatic drafting, were associated with 43% higher labour efficiency (cows milked/h.person) and 14% higher milking efficiency (cows milked/h) than were rotary dairies without all three technologies. Dairy farmers will increasingly use technologies that deliver value, and the present study has provided information to guide investment decisions, product development and research in areas such as applying technology in new workplaces.

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SEDOV, ALEKSEY. "DEVELOPMENT OF AN INTELLIGENT MACHINE FOR SORTING ANIMALS ACCORDING TO SPECIFIED CRITERIA." Elektrotekhnologii i elektrooborudovanie v APK 4, no. 41 (December 2020): 83–87. http://dx.doi.org/10.22314/2658-4859-2020-67-4-83-87.

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The Federal scientific Agroengineering center VIM has developed technical tools, algorithms and software for the intelligent automatic control system for milking animals “Stimul” on the “Herringbone” milking unit in three versions. The created system does not include automatic selection gates for effective management of zootechnical and veterinary services of animals. (Research purpose) The research purpose is in developing an intelligent machine for automatic sorting of animals for servicing and managing the herd according to specified characteristics. (Materials and methods) The article presents the development of control and management systems in dairy farming based on the conceptual principles of digital transformation. The digital control system is based on a multifunctional panel controller. The created control unit has a port for connecting to the RS 485 network and provides support for network functions via the Modbus Protocol. The programming of the control unit has been made in the SMLogix tool environment, which supports the FBD function block language. (Results and discussion) The article presents an intelligent machine for automatic sorting of animal flows for servicing and managing the herd according to specified characteristics with the unification of hardware, software modules and interface. The article describes the necessary parameters for the automatic remote animal identification system, the basic component of the control system of an intelligent machine for sorting animals according to specified characteristics. (Conclusions) The machine allows to automatically identify, sort and send animals to the specified areas for individual service.

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Barge, P., P. Gay, V. Merlino, and C. Tortia. "Radio frequency identification technologies for livestock management and meat supply chain traceability." Canadian Journal of Animal Science 93, no. 1 (March 2013): 23–33. http://dx.doi.org/10.4141/cjas2012-029.

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Barge, P., Gay, P., Merlino, V. and Tortia, C. 2013. Radio frequency identification technologies for livestock management and meat supply chain traceability. Can. J. Anim. Sci. 93: 23–33. Animal electronic identification could be exploited by farmers as an interesting opportunity to increase the efficiency of herd management and traceability. Although radio frequency identification (RFID) solutions for animal identification have already been envisaged, the integration of a RFID traceability system at farm level has to be carried out carefully, considering different aspects (farm type, number and species of animals, barn structure). The tag persistence on the animal after application, the tag-to-tag collisions in the case of many animals contemporarily present in the reading area of the same antenna and the barn layout play determinant roles in system reliability. The goal of this paper is to evaluate the RFID identification system performance and determine the best practice to apply these devices in livestock management. RFID systems were tested both in laboratory, on the farm and in slaughterhouses for the implementation of a traceability system with automatic animal data capture. For this purpose a complete system for animal identification and tracking, accomplishing regulatory compliance as well as supply chain management requirements, has been developed and is described in the paper. Results were encouraging for identification of calves both in farms and slaughterhouses, while in swine breeding, identification was critical for small piglets. In this case, the design of a RFID gate where tag-to-tag collisions are avoided should be envisaged.

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Manohar, N., Y. H. Sharath Kumar, and G. Hemantha Kumar. "An Approach for the Development of Animal Tracking System." International Journal of Computer Vision and Image Processing 8, no. 1 (January 2018): 15–31. http://dx.doi.org/10.4018/ijcvip.2018010102.

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In this article, the authors propose a system which can identify and track animals. Identification and tracking of animals has got plenty of applications like, avoiding dangerous animal intrusion into residential areas, avoiding animal-vehicle collisions, and behavioral study of animals and so on. Previously, biologists studied videos to detect and identify animals, a time consuming and difficult task. This requires a fully automatic or computer-assisted system to identify and track animals by video. Initially, frames are extracted from the given video. Segmentation is done to the extracted frames using a maximum similarity-based region merging algorithm. Then, the mean shift-based algorithm is used to track the animals. Finally, the animals are classified using Gabor features and a KNN classifier. Experimentation has been conducted on a data set containing more than 150 videos with 15 different classes.

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Xu, Weitao, Xiang Zhang, Lina Yao, Wanli Xue, and Bo Wei. "A multi-view CNN-based acoustic classification system for automatic animal species identification." Ad Hoc Networks 102 (May 2020): 102115. http://dx.doi.org/10.1016/j.adhoc.2020.102115.

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Beiderman, Yevgeny, Mark Kunin, Eli Kolberg, Ilan Halachmi, Binyamin Abramov, Rafael Amsalem, and Zeev Zalevsky. "Automatic solution for detection, identification and biomedical monitoring of a cow using remote sensing for optimised treatment of cattle." Journal of Agricultural Engineering 45, no. 4 (December 21, 2014): 153. http://dx.doi.org/10.4081/jae.2014.418.

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In this paper we show how a novel photonic remote sensing system assembled on a robotic platform can extract vital biomedical parameters from cattle including their heart beating, breathing and chewing activity. The sensor is based upon a camera and a laser using selfinterference phenomena. The whole system intends to provide an automatic solution for detection, identification and biomedical monitoring of a cow. The detection algorithm is based upon image processing involving probability map construction. The identification algorithms involve well known image pattern recognition techniques. The sensor is used on top of an automated robotic platform in order to support animal decision making. Field tests and computer simulated results are presented.

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Kucevic, Denis, S. Trivunovic, M. Plavsic, S. Stankovski, and G. Ostojic. "Modern aspects of marking of animals." Biotehnologija u stocarstvu 25, no. 1-2 (2009): 153–59. http://dx.doi.org/10.2298/bah0902153k.

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The conventional marking and identification of animals can be done in several different ways. With the application of modern informatics and electronics solutions, it is possible to substitute conventional ways with the different types of the electronic marking and identification. All types of electronic identification for transferring data are using the technology of the radio frequency (RFDI). With application of electronic marking, it is possible to achieve a great number of advantages of which the most important are the high precision of reading the data, individual supervision for every animal, automatic input of data, processing and keeping the information as a permanent actualization of data base. It is necessary to remove all existing defects and in future to work on the improvement of existing types of the electronic marking of animals. .

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Petso, Tinao, Rodrigo S. Jamisola, Dimane Mpoeleng, Emily Bennitt, and Wazha Mmereki. "Automatic animal identification from drone camera based on point pattern analysis of herd behaviour." Ecological Informatics 66 (December 2021): 101485. http://dx.doi.org/10.1016/j.ecoinf.2021.101485.

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Gomez Villa, Alexander, Augusto Salazar, and Francisco Vargas. "Towards automatic wild animal monitoring: Identification of animal species in camera-trap images using very deep convolutional neural networks." Ecological Informatics 41 (September 2017): 24–32. http://dx.doi.org/10.1016/j.ecoinf.2017.07.004.

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Peng, Chengbin, Carlos M. Duarte, Daniel P. Costa, Christophe Guinet, Robert G. Harcourt, Mark A. Hindell, Clive R. McMahon, et al. "Deep Learning Resolves Representative Movement Patterns in a Marine Predator Species." Applied Sciences 9, no. 14 (July 23, 2019): 2935. http://dx.doi.org/10.3390/app9142935.

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The analysis of animal movement from telemetry data provides insights into how and why animals move. While traditional approaches to such analysis mostly focus on predicting animal states during movement, we describe an approach that allows us to identify representative movement patterns of different animal groups. To do this, we propose a carefully designed recurrent neural network and combine it with telemetry data for automatic feature extraction and identification of non-predefined representative patterns. In the experiment, we consider a particular marine predator species, the southern elephant seal, as an example. With our approach, we identify that the male seals in our data set share similar movement patterns when they are close to land. We identify this pattern recurring in a number of distant locations, consistent with alternative approaches from previous research.

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Arsevska, Elena, Mathieu Roche, Pascal Hendrikx, David Chavernac, Sylvain Falala, Renaud Lancelot, and Barbara Dufour. "Identification of Associations between Clinical Signs and Hosts to Monitor the Web for Detection of Animal Disease Outbreaks." International Journal of Agricultural and Environmental Information Systems 7, no. 3 (July 2016): 1–20. http://dx.doi.org/10.4018/ijaeis.2016070101.

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In a context of intensification of international trade and travels, the transboundary spread of emerging human or animal pathogens represents a growing concern. One of the missions of the national veterinary services is to implement international epidemiological intelligence for a timely and accurate detection of emerging animal infectious diseases (EAID) worldwide, and take early actions to prevent their introduction on the national territory. For this purpose, an efficient use of the information published on the web is essential. The authors present a comprehensive method for identification of relevant associations between terms describing clinical signs and hosts to build queries to monitor the web for early detection of EAID. Using text and web mining approaches, they present statistical measures for automatic selection of relevant associations between terms. In addition, expert elicitation is used to highlight the most relevant terms and associations among those automatically selected. The authors assessed the performance of the combination of the automatic approach and expert elicitation to monitor the web for a list of selected animal pathogens.

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Ghosh, Pritam, Subhranil Mustafi, and Satyendra Nath Mandal. "Image-Based Goat Breed Identification and Localization Using Deep Learning." International Journal of Computer Vision and Image Processing 10, no. 4 (October 2020): 74–96. http://dx.doi.org/10.4018/ijcvip.2020100105.

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In this paper an attempt has been made to identify six different goat breeds from pure breed goat images. The images of goat breeds have been captured from different organized registered goat farms in India, and almost two thousand digital images of individual goats were captured in restricted (to get similar image background) and unrestricted (natural) environments without imposing stress to animals. A pre-trained deep learning-based object detection model called Faster R-CNN has been fine-tuned by using transfer-learning on the acquired images for automatic classification and localization of goat breeds. This fine-tuned model is able to locate the goat (localize) and classify (identify) its breed in the image. The Pascal VOC object detection evaluation metrics have been used to evaluate this model. Finally, comparison has been made with prediction accuracies of different technologies used for different animal breed identification.

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Jeantet, Lorène, Víctor Planas-Bielsa, Simon Benhamou, Sebastien Geiger, Jordan Martin, Flora Siegwalt, Pierre Lelong, et al. "Behavioural inference from signal processing using animal-borne multi-sensor loggers: a novel solution to extend the knowledge of sea turtle ecology." Royal Society Open Science 7, no. 5 (May 2020): 200139. http://dx.doi.org/10.1098/rsos.200139.

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The identification of sea turtle behaviours is a prerequisite to predicting the activities and time-budget of these animals in their natural habitat over the long term. However, this is hampered by a lack of reliable methods that enable the detection and monitoring of certain key behaviours such as feeding. This study proposes a combined approach that automatically identifies the different behaviours of free-ranging sea turtles through the use of animal-borne multi-sensor recorders (accelerometer, gyroscope and time-depth recorder), validated by animal-borne video-recorder data. We show here that the combination of supervised learning algorithms and multi-signal analysis tools can provide accurate inferences of the behaviours expressed, including feeding and scratching behaviours that are of crucial ecological interest for sea turtles. Our procedure uses multi-sensor miniaturized loggers that can be deployed on free-ranging animals with minimal disturbance. It provides an easily adaptable and replicable approach for the long-term automatic identification of the different activities and determination of time-budgets in sea turtles. This approach should also be applicable to a broad range of other species and could significantly contribute to the conservation of endangered species by providing detailed knowledge of key animal activities such as feeding, travelling and resting.

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Kojima, Ryosuke, Osamu Sugiyama, Kotaro Hoshiba, Kazuhiro Nakadai, Reiji Suzuki, and Charles E. Taylor. "Bird Song Scene Analysis Using a Spatial-Cue-Based Probabilistic Model." Journal of Robotics and Mechatronics 29, no. 1 (February 20, 2017): 236–46. http://dx.doi.org/10.20965/jrm.2017.p0236.

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[abstFig src='/00290001/22.jpg' width='300' text='Spatial-cue-based probabilistic model' ] This paper addresses bird song scene analysis based on semi-automatic annotation. Research in animal behavior, especially in birds, would be aided by automated or semi-automated systems that can localize sounds, measure their timing, and identify their sources. This is difficult to achieve in real environments, in which several birds at different locations may be singing at the same time. Analysis of recordings from the wild has usually required manual annotation. These annotations may be inaccurate or inconsistent, as they may vary within and between observers. Here we suggest a system that uses automated methods from robot audition, including sound source detection, localization, separation and identification. In robot audition, these technologies are assessed separately, but combining them has often led to poor performance in natural setting. We propose a new Spatial-Cue-Based Probabilistic Model (SCBPM) for their integration focusing on spatial information. A second problem has been that supervised machine learning methods usually require a pre-trained model, which may need a large training set of annotated labels. We have employed a semi-automatic annotation approach, in which a semi-supervised training method is deduced for a new model. This method requires much less pre-annotation. Preliminary experiments with recordings of bird songs from the wild revealed that our system outperformed the identification accuracy of a method based on conventional robot audition.**This paper is an extension of a proceeding of IROS2015.

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Nepovinnykh, Ekaterina, Tuomas Eerola, Vincent Biard, Piia Mutka, Marja Niemi, Mervi Kunnasranta, and Heikki Kälviäinen. "SealID: Saimaa Ringed Seal Re-Identification Dataset." Sensors 22, no. 19 (October 7, 2022): 7602. http://dx.doi.org/10.3390/s22197602.

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Wildlife camera traps and crowd-sourced image material provide novel possibilities to monitor endangered animal species. The massive data volumes call for automatic methods to solve various tasks related to population monitoring, such as the re-identification of individual animals. The Saimaa ringed seal (Pusa hispida saimensis) is an endangered subspecies only found in Lake Saimaa, Finland, and is one of the few existing freshwater seal species. Ringed seals have permanent pelage patterns that are unique to each individual and that can be used for the identification of individuals. A large variation in poses, further exacerbated by the deformable nature of seals, together with varying appearance and low contrast between the ring pattern and the rest of the pelage makes the Saimaa ringed seal re-identification task very challenging, providing a good benchmark by which to evaluate state-of-the-art re-identification methods. Therefore, we make our Saimaa ringed seal image (SealID) dataset (N = 57) publicly available for research purposes. In this paper, the dataset is described, the evaluation protocol for re-identification methods is proposed, and the results for two baseline methods—HotSpotter and NORPPA—are provided. The SealID dataset has been made publicly available.

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YEN, GARY G., and QIANG FU. "AUTOMATIC FROG CALLS MONITORING SYSTEM: A MACHINE LEARNING APPROACH." International Journal of Computational Intelligence and Applications 01, no. 02 (June 2001): 165–86. http://dx.doi.org/10.1142/s1469026801000184.

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Automatic recognition of frog vocalization is considered a valuable tool for a variety of biological research and environmental monitoring applications. In this research an automatic monitoring system, which can recognize the vocalizations of four species of frogs and can identify different individuals within the species of interest, is proposed. For the desired monitoring system, species identification is performed first with the proposed filtering and grouping algorithm. Individual identification, which can estimate frog population within the specific species, is performed in the second stage. Digital signal pre-processing, feature extraction, dimensionality reduction, and neural network pattern classification are performed step by step in this stage. Wavelet Packet feature extraction together with two different dimension reduction algorithms are synergistically integrated to produce final feature vectors, which are to be fed into a neural network classifier. The simulation results show the promising future of deploying an array of continuous, on-line environmental monitoring systems based upon nonintrusive analysis of animal calls.

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Mohammed, Hind R., and Zahir M. Hussain. "Hybrid Mamdani Fuzzy Rules and Convolutional Neural Networks for Analysis and Identification of Animal Images." Computation 9, no. 3 (March 17, 2021): 35. http://dx.doi.org/10.3390/computation9030035.

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Accurate, fast, and automatic detection and classification of animal images is challenging, but it is much needed for many real-life applications. This paper presents a hybrid model of Mamdani Type-2 fuzzy rules and convolutional neural networks (CNNs) applied to identify and distinguish various animals using different datasets consisting of about 27,307 images. The proposed system utilizes fuzzy rules to detect the image and then apply the CNN model for the object’s predicate category. The CNN model was trained and tested based on more than 21,846 pictures of animals. The experiments’ results of the proposed method offered high speed and efficiency, which could be a prominent aspect in designing image-processing systems based on Type 2 fuzzy rules characterization for identifying fixed and moving images. The proposed fuzzy method obtained an accuracy rate for identifying and recognizing moving objects of 98% and a mean square error of 0.1183464 less than other studies. It also achieved a very high rate of correctly predicting malicious objects equal to recall = 0.98121 and a precision rate of 1. The test’s accuracy was evaluated using the F1 Score, which obtained a high percentage of 0.99052.

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Tzanidakis, Christos, Ouranios Tzamaloukas, Panagiotis Simitzis, and Panagiotis Panagakis. "Precision Livestock Farming Applications (PLF) for Grazing Animals." Agriculture 13, no. 2 (January 25, 2023): 288. http://dx.doi.org/10.3390/agriculture13020288.

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Over the past four decades the dietary needs of the global population have been elevated, with increased consumption of animal products predominately due to the advancing economies of South America and Asia. As a result, livestock production systems have expanded in size, with considerable changes to the animals’ management. As grazing animals are commonly grown in herds, economic and labour constraints limit the ability of the producer to individually assess every animal. Precision Livestock Farming refers to the real-time continuous monitoring and control systems using sensors and computer algorithms for early problem detection, while simultaneously increasing producer awareness concerning individual animal needs. These technologies include automatic weighing systems, Radio Frequency Identification (RFID) sensors for individual animal detection and behaviour monitoring, body temperature monitoring, geographic information systems (GIS) for pasture evaluation and optimization, unmanned aerial vehicles (UAVs) for herd management, and virtual fencing for herd and grazing management. Although some commercial products are available, mainly for cattle, the adoption of these systems is limited due to economic and cultural constraints and poor technological infrastructure. This review presents and discusses PLF applications and systems for grazing animals and proposes future research and strategies to improve PLF adoption and utilization in today’s extensive livestock systems.

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Xu, Beibei, Wensheng Wang, Leifeng Guo, Guipeng Chen, Yaowu Wang, Wenju Zhang, and Yongfeng Li. "Evaluation of Deep Learning for Automatic Multi-View Face Detection in Cattle." Agriculture 11, no. 11 (October 28, 2021): 1062. http://dx.doi.org/10.3390/agriculture11111062.

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Individual identification plays an important part in disease prevention and control, traceability of meat products, and improvement of agricultural false insurance claims. Automatic and accurate detection of cattle face is prior to individual identification and facial expression recognition based on image analysis technology. This paper evaluated the possibility of the cutting-edge object detection algorithm, RetinaNet, performing multi-view cattle face detection in housing farms with fluctuating illumination, overlapping, and occlusion. Seven different pretrained CNN models (ResNet 50, ResNet 101, ResNet 152, VGG 16, VGG 19, Densenet 121 and Densenet 169) were fine-tuned by transfer learning and re-trained on the dataset in the paper. Experimental results showed that RetinaNet incorporating the ResNet 50 was superior in accuracy and speed through performance evaluation, which yielded an average precision score of 99.8% and an average processing time of 0.0438 s per image. Compared with the typical competing algorithms, the proposed method was preferable for cattle face detection, especially in particularly challenging scenarios. This research work demonstrated the potential of artificial intelligence towards the incorporation of computer vision systems for individual identification and other animal welfare improvements.

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Ali, R., M. Dorozynski, J. Stracke, and M. Mehltretter. "DEEP LEARNING-BASED TRACKING OF MULTIPLE OBJECTS IN THE CONTEXT OF FARM ANIMAL ETHOLOGY." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2022 (May 30, 2022): 509–16. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2022-509-2022.

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Abstract. Automatic detection and tracking of individual animals is important to enhance their welfare and to improve our understanding of their behaviour. Due to methodological difficulties, especially in the context of poultry tracking, it is a challenging task to automatically recognise and track individual animals. Those difficulties can be, for example, the similarity of animals of the same species which makes distinguishing between them harder, or sudden changes in their body shape which may happen due to putting on or spreading out the wings in a very short period of time. In this paper, an automatic poultry tracking algorithm is proposed. This algorithm is based on the well-known tracktor approach and tackles multi-object tracking by exploiting the regression head of the Faster R-CNN model to perform temporal realignment of object bounding boxes. Additionally, we use a multi-scale re-identification model to improve the re-association of the detected animals. For evaluating the performance of the proposed method in this study, a novel dataset consisting of seven image sequences that show chicks in an average pen farm in different stages of growth is used.

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ROMANENKO, Tetiana, and Nataliia RUSINA. "USE OF VISUAL PROGRAMMING LANGUAGE FOR SIMULATION OF DYNAMIC SYSTEMS." HERALD OF KHMELNYTSKYI NATIONAL UNIVERSITY 295, no. 2 (May 2021): 109–15. http://dx.doi.org/10.31891/2307-5732-2021-295-2-109-115.

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The article presents examples of research of typical links of linear systems and construction and study of transient functions, namely: research of influence of parameters of elements of systems of automatic control of its quality. Programs for automatic control are developing rapidly, the main areas of which are related to the optimization of technological processes and robotics. This encourages the introduction into modern production of high-precision digital systems with more extensive use of computer systems. In the simulation process, there is often a need to carefully select and apply real objects to study the quality of automatic control systems. This can be achieved by using a visual programming language for modeling dynamic systems and designing VisSim. The connection of parameters of automatic control systems with indicators of its quality is investigated: by definition of error coefficient; research of influence of a constant time of a forcing link on quality of automatic control systems by the method of compensation of the part in the main inertia of the control object, for the use of the forcing link. As a result, of research graphic dependences of quality of linear systems of automatic control, research of influence of a constant of time of a forcing link on its quality, carrying out identification of the regulator and object of management of systems of automatic control are received. Studies of the process of modeling dynamic systems were visually presented using the visual programming language VisSim. In particular, by creating virtual laboratory stands to study the quality of different modes of automatic control systems in relation to the performance of signal generators and the calculation of the necessary parameters of the study.

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Rotger, Andreu. "Photo-identification of horseshoe whip snakes (Hemorrhois hippocrepis, Linnaeus, 1758) by a semi-automatic procedure applied to wildlife management." Herpetological Journal, Volume 29, Number 4 (October 1, 2019): 304–7. http://dx.doi.org/10.33256/29.4.304307.

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Photo-identification is an increasingly used method for the study of animal populations. Natural marks such as coloration or scale pattern to identify individuals provide an inexpensive and less invasive alternative to conventional tagging methods. Photo-identification has previously been used to distinguish individual snakes, usually by comparing the pileus region. Nevertheless, this method is seldom used in capture-recapture studies. We show the effectiveness of photo-identification in snakes using specific software for individual recognition applied to a wildlife control study of horseshoe whip snakes. Photos were analysed with Automatic Photo Identification Suite (APHIS), which allowed us to compare the variability of head scale patterns surrounding the parietal shields instead of the traditional method of using large scale groups of the pileus. APHIS correctly identified 100 % of recaptures of snakes. Although further studies are needed, the variability of the surrounding scales of the pileus region seems a robust method to identify and differentiate individuals.

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Vagić, Nemanja, Aleksandar Peulić, and Sanja Stojković. "Object detection in order to determine locations for wildlife crossings." Zbornik radova - Geografski fakultet Univerziteta u Beogradu, no. 70 (2022): 23–36. http://dx.doi.org/10.5937/zrgfub2270023v.

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The intensive construction of road infrastructure due to urbanization and industrialization around the world carries with it negative environmental impacts, primarily due to increased emissions of gases, but also due to the separation of natural habitats and ecosystems. In order to overcome this problem, without affecting the mobility of the population, it is necessary to allow wild animals to cross over or below the roads, i.e. to create wildlife crossings, which requires knowledge of the locations where the corridors of animal movements intersect with existing or planned roads. This paper analysis the establishment of a camera system and the application of a deep learning methodology for the automatic identification of animals by species and number, in order to determine locations for the construction of crossings for large wildlife. Also, the paper presents the possibility of using geographic information systems to analyze information obtained by monitoring built wildlife crossings.

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Lu, Kai, Yueqi Zhong, Duan Li, Xinyu Chai, Haoyang Xie, Zhicai Yu, and Tayyab Naveed. "Cashmere/wool identification based on bag-of-words and spatial pyramid match." Textile Research Journal 88, no. 21 (August 10, 2017): 2435–44. http://dx.doi.org/10.1177/0040517517723027.

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Due to the similarities between cashmere and wool, the automatic identification of these two animal fibers continues to be a huge challenge in textile society. In this paper, for the identification of micrographs of cashmere and wool, bag-of-words and spatial pyramid matching are used. Each fiber image was regarded as a collection of feature vectors in our logic. The vectors, extracted from the original dataset, were fed into a support vector machine for supervised classification. The codebook size and the resolution level were completely investigated. The experimental results indicated that the image segmentation delivered a positive contribution in enhancing the accuracy of classification. The overall performance of the model was robust under various blend ratios. It verifies that the bag-of-words with spatial pyramid match is an effective approach to the identification of cashmere and wool fibers.

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Jiang, Xian, Tingdong Yang, Dongping Liu, Yili Zheng, Yan Chen, and Fan Li. "An Automatic Identification Method of Crested Ibis (Nipponia nippon) Habitat Based on Spatiotemporal Density Detection." Animals 12, no. 17 (August 29, 2022): 2220. http://dx.doi.org/10.3390/ani12172220.

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To address the current challenges of the heavy workload, time-consuming nature and labor-intensiveness involved in existing crested ibis’s (Nipponia nipponTemminck, 1835) habitat identification approaches, this paper proposes an automatic habitat identification method based on spatiotemporal density detection. With consideration of the characteristics of the crested ibis’s trajectory data, such as aggregation, repeatability, and uncertainty, this method achieves detecting the crested ibis’s stopping points by using the spatial characteristics of the trajectory data. On this basis, an improved spatiotemporal clustering-based DBSCAN method is proposed in this paper, incorporating temporal characteristics of the trajectory data. By combining the spatial and temporal features, the proposed method is able to accurately identify the roosting and foraging sites among the crested ibis’s stopping points. Supported by remote sensing images and field investigations, it was found that the method proposed in this paper has a good clustering effect and can effectively identify the crested ibis’s foraging sites and overnight roosting areas. Specifically, the woodland, farmland, and river areas are the common foraging sites for the crested ibis, while the woodland with large trees is their common overnight site. Therefore, the method proposed in this paper can provide technical support for identifying and protecting the crested ibis’s habitats.

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Wang, Yingying, Jixiang Du, Hongbo Zhang, and Xiuhong Yang. "Mushroom Toxicity Recognition Based on Multigrained Cascade Forest." Scientific Programming 2020 (August 1, 2020): 1–13. http://dx.doi.org/10.1155/2020/8849011.

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Due to the tastiness of mushroom, this edible fungus often appears in people’s daily meals. Nevertheless, there are still various mushroom species that have not been identified. Thus, the automatic identification of mushroom toxicity is of great value. A number of methods are commonly employed to recognize mushroom toxicity, such as folk experience, chemical testing, animal experiments, and fungal classification, all of which cannot produce quick, accurate results and have a complicated cycle. To solve these problems, in this paper, we proposed an automatic toxicity identification method based on visual features. The proposed method regards toxicity identification as a binary classification problem. First, intuitive and easily accessible appearance data, such as the cap shape and color of mushrooms, were taken as features. Second, the missing data in any of the features were handled in two ways. Finally, three pattern-recognition methods, including logistic regression, support vector machine, and multigrained cascade forest, were used to construct 3 different toxicity classifiers for mushrooms. Compared with the logistic regression and support vector machine classifiers, the multigrained cascade forest classifier had better performance with an accuracy of approximately 98%, enhancing the possibility of preventing food poisoning. These classifiers can recognize the toxicity of mushrooms—even that of some unknown species—according to their appearance features and important social and application value.

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Strus, V., and Iu Strus. "Methods of amphibian tracking." Studia Biologica 14, no. 4 (December 2020): 69–84. http://dx.doi.org/10.30970/sbi.1404.639.

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ntroduction: In this article, we present a literature review of modern, most common, and useful methods of amphibian tracking. Amphibians are the most sensitive group of animals and near 41 % of species are under the risk of extinction. Therefore, the research of spatial movements of amphibians is one of the most topical tasks of modern herpetology. The information about the use of space is needed for practical protection of sensitive species of amphibians, for planning of protected areas, estimation of the potential danger for some groups of animals when designing infrastructure objects, such as roads. The movement studies gained popularity and have been undertaken since the second part of the 20th century. Such growth in the number of studies is related to the recent advances in radio-electronic technology that contributed to the creation of a range of instruments for automatic or semi-automatic tracking of individual animals. Unfortunately, most of such methods are still too expensive for scientists from developing countries. Thus, classical methods are still widely used. Results: We describe six methods of amphibians tracking: radioisotope tracking, automated radio telemetry, harmonic direction finding, radio frequency identification, fluorescent powder, spool tracking. Each of these methods allows collecting detailed information about spatial movements of individuals. Many of these tracking methods require using of a special tag, which is attached to an animal and used for its further detection. Different types of tags have identical functions but are based on different principles of use. Two of the described methods do not require using of tags and are cheap. These are fluorescent powder and spool tracking. In the article, we provide many links to grant programs and places where special equipment can be found. Conclusions: As a result of literature review, we created a table with concise information about all the described methods. Using this table one can choose the best method for an experiment. Keywords: amphibians, radiotelemetry, fluorescent powder, radioisotope tracking, spool tracking

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Ruiz de la Hermosa, A., F. Truyols-Hermosa, and S. Pinya. "Individual photographic identification based on unique colour pattern of the thorax of Acherontia atropos (Linnaeus, 1758) (Lepidoptera: Sphingidae)." SHILAP Revista de lepidopterología 50, no. 197 (March 30, 2022): 33–41. http://dx.doi.org/10.57065/shilap.181.

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Natural marks have increasingly been used as a tool for individual identification. One of the most popular techniques used by natural marks as an individual recognition tool is photo-identification. Photo-identification is a non-invasive alternative to traditional marking, which allows individual recognition of species through time and space. In this study, the APHIS (Automatic Photo Identification Suite) software has been evaluated as software capable of identifying individuals of Acherontia atropos (Linnaeus, 1758). The SPM (Spot Pattern Matching) and ITM (Image Template Matching) procedures were tested and found to achieve 100% success of individuals recognition. Thus, for the first time in a Sphingidae, the colour pattern of the dorsal part of the thorax of A. atropos is demonstrated to represent a suitable natural mark for individual recognition.

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Luo, Wei, Ze Zhang, Ping Fu, Guosheng Wei, Dongliang Wang, Xuqing Li, Quanqin Shao, et al. "Intelligent Grazing UAV Based on Airborne Depth Reasoning." Remote Sensing 14, no. 17 (August 25, 2022): 4188. http://dx.doi.org/10.3390/rs14174188.

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The existing precision grazing technology helps to improve the utilization rate of livestock to pasture, but it is still at the level of “collectivization” and cannot provide more accurate grazing management and control. (1) Background: In recent years, with the rapid development of agent-related technologies such as deep learning, visual navigation and tracking, more and more lightweight edge computing cell target detection algorithms have been proposed. (2) Methods: In this study, the improved YOLOv5 detector combined with the extended dataset realized the accurate identification and location of domestic cattle; with the help of the kernel correlation filter (KCF) automatic tracking framework, the long-term cyclic convolution network (LRCN) was used to analyze the texture characteristics of animal fur and effectively distinguish the individual cattle. (3) Results: The intelligent UAV equipped with an AGX Xavier high-performance computing unit ran the above algorithm through edge computing and effectively realized the individual identification and positioning of cattle during the actual flight. (4) Conclusion: The UAV platform based on airborne depth reasoning is expected to help the development of smart ecological animal husbandry and provide better precision services for herdsmen.

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CHEN, LI-PING, YA-GE CHANG, JIAN LI, JIAN-MEI WANG, JIN-LIN LIU, YONG-CHAO ZHI, and XIN-JIANG LI. "Application of DNA Barcoding in the Classification of Grasshoppers (Orthoptera: Acridoidea)—A Case Study of grasshoppers from Hebei Province, China." Zootaxa 4497, no. 1 (October 8, 2018): 99. http://dx.doi.org/10.11646/zootaxa.4497.1.6.

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Grasshoppers (Orthoptera: Acridoidea) are the main pests in agriculture, animal husbandry and forestry, and some species of grasshoppers can cause serious disaster. Taxonomy is the basis of pest control. Traditional morphological identification is time-consuming and laborious. It may be due to the existence of cryptic species or the limited number of morphologists, making the identification extremely unstable. In recent years, with the development of molecular systematics, DNA barcoding technology has been applied to environment, ecology, quarantine and so on. This study focuses on testing the feasibility of DNA barcoding in the species identification for superfamily Acridoidea. Sequences of the cox1 gene were obtained from 245 individuals of 43 species of Acridoidea and one species of Tetrigoidea as outgroup from Hebei Province. Phylogenetic, genetic distance and sequence difference threshold analyses using the Maximum Likelihood (ML), Automatic Barcode Gap Discovery (ABGD) and Molecular Defined Operational Taxonomic Units (MOTU) methods, respectively, were performed for obtained sequences and the 139 additional sequences of 21 species downloaded from GenBank. The results have shown that 40, 33, and 35 species among the 48 species are consistent with the traditional morphological classification based on the phylogenetic tree, ABGD and MOTU results, respectively and the DNA barcoding technology is very efficient and helpful for identifying the species of the superfamily Acridoidea; however, the morphological approach is still playing a key role in the species identifications. It also indicates that the cox1 gene is suitable for the phylogeny of genera and species level, but it is not suitable for the phylogenetic relationship of the advanced taxa such as families.

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Kayci, Lokman, and Yılmaz Kaya. "A vision system for automatic identification of butterfly species using a grey-level co-occurrence matrix and multinomial logistic regression." Zoology in the Middle East 60, no. 1 (January 2, 2014): 57–64. http://dx.doi.org/10.1080/09397140.2014.892340.

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Wieland, Matthias, Paul Douglas Virkler, and Anja Sipka. "Risk Factors of Forced Take-Off in Dairy Cows Milked Three Times per Day in A Rotary Milking Parlor: A Case Control Study." Animals 11, no. 10 (October 3, 2021): 2883. http://dx.doi.org/10.3390/ani11102883.

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The aims of the research were to: (1) describe a protocol for the identification of cows that are subjected repeatedly to a forced retraction event at the end of milking; (2) study risk factors of repeated forced take-off (RFTO); and (3) assess the average milk flow rate at which the forced retraction event occurred. In a retrospective study, we collected milk flow data over a 1-week period from a 4300-cow dairy with a rotary milking parlor and a thrice-daily milking schedule. We identified 109 cases of RFTO and 2467 controls. A multivariable logistic regression model revealed associations of parity, stage of lactation, average daily milk production, and milking speed with RFTO. Cows in parity 3 or greater, animals ≤100 days in milk, high-producing animals, and cows with low milking speed had higher odds of RFTO. The average (least squares means (95% CI)) milk flow rates at the time of removal of the milking unit were 2.1 (2.0–2.1) kg/min in milking observations that were terminated with the forced retract and 1.5 (1.4–1.5) kg/min when milking units were removed with the automatic cluster remover. Future research to better understand the effect of RFTO on milk production, udder health, and animal well-being is warranted.

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Kovalyev, I. L., and M. N. Kostomakhin. "Development vectors and foreign experience of information technologies in the agro-industrial complex of Russia and Belarus." Glavnyj zootehnik (Head of Animal Breeding), no. 1 (January 1, 2021): 49–61. http://dx.doi.org/10.33920/sel-03-2101-06.

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The current stage of information technology development is characterized as digital called BCG (Boston Consulting Group) digitalization, while the analog period in agriculture is over, the industry has entered the digital era, which means that by 2050 the use of new generation technologies will be able to increase the productivity of world agriculture by 70 %. The main stages of information technology development in the world considers some of the most important areas of it technology development and global trends in the digital transformation of the agro-industrial complex based on the analysis of global scientific achievements, research reports, articles by well-known scientists, scientific and expert organizations have been investigated in the article. The main trends that determine the conceptual development of the so-called “Smart (digital) agriculture” are identified, the active use of elements of which contributes in every possible way to the highly rational social, economic, technical and technological development of the agricultural sector. A promising area is Precision Animal Husbandry (similar to Precision Farming). Among the elements of Precision Animal Husbandry the most widely used are identification and monitoring of individual animals using modern information technologies (feeding ration, milk yield, growth, body temperature, activity), meeting their individual needs; automatic regulation of the microclimate and control of harmful gases; monitoring the health of the herd, product quality; electronic database of the production process; robotization of the milking process.

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Nye, Jessica, Laura Zingaretti, and Miguel Perez-Enciso. "224 Automatic image feature extraction for genetic analysis in cattle." Journal of Animal Science 97, Supplement_3 (December 2019): 47. http://dx.doi.org/10.1093/jas/skz258.093.

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Abstract Image analysis has increasingly become an important tool for increasing productivity in many industries, yet its application in breeding programs is under utilized. With coat color patterns from dairy bull images, we explore automatic image analysis that extracts features which can be used in genetic analysis. In order to remove the unnecessary background information, the current methods require time consuming human inspection. Here, we present and compare a composite method that creates a mask (i.e., removes the background portion of the image) and calculates the proportion of dark and light coloration in bulls (n = 657) from the breeds Holstein and Ayrshire in dynamic backgrounds (e.g., forest, grass, hay, snow, etc.). This composite method combines the supervised algorithm MASK-RCNN, an unsupervised image segmentation approach, and k-means color clustering. The first step identifies the region of interest removing the majority of the background noise, while the second and third steps optimize the identification of the bull and segments the color patterning. We find a very low discrepancy between the proportion of white and dark between the manual curation and the composite method (+/- 1.40%); with an immense reduction in data collection time. This automatic composite method greatly improves the efficiency of complex image segmentation and analysis without compromising the quality of the data extracted, making analysis computationally feasible for large data sets. The next step is to calculate genetic parameters from these extracted phenotypes with genomic and/or pedigree data.

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ANAK ENTUNI, CHYNTIA JABY, and TENGKU MOHD AFENDI ZULCAFFLE. "Identification of Corn Leaf Diseases Comprising of Blight, Grey Spot and Rust Using DenseNet-201." Borneo Journal of Resource Science and Technology 12, no. 1 (June 30, 2022): 125–34. http://dx.doi.org/10.33736/bjrst.4224.2022.

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Corn is a vital commodity in Malaysia because it is a key component of animal feed. The retention of the wholesome corn yield is essential to satisfy the rising demand. Like other plants, corn is susceptible to pathogens infection during the growing period. Manual observation of the diseases nevertheless takes time and requires a lot of work. The aim of this study was to propose an automatic approach to identify corn leaf diseases. The dataset used comprises of the images of diseased corn leaf comprising of blight, grey spot and rust as well as healthy corn leaf in YCbCr colour space representation. The DenseNet-201 algorithm was utilised in the proposed method of identifying corn leaf diseases. The training and validation analysis of distinctive epoch values of DenseNet-201 were also used to validate the proposed method, which resulted in significantly higher identification accuracy. DenseNet-201 succeeded 95.11% identification accuracy and it outperformed the prior identification methods such as ResNet-50, ResNet-101 and Bag of Features. The DenseNet-201 also has been validated to function as anticipated in identifying corn leaf diseases based on the algorithm validation assessment.

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EVANS, MEGAN L., SIOBHON EGAN, PETER J. IRWIN, and CHARLOTTE L. OSKAM. "Automatic Barcode Gap Discovery reveals large COI intraspecific divergence in Australian Ixodidae." Zootaxa 4656, no. 2 (August 14, 2019): 393–96. http://dx.doi.org/10.11646/zootaxa.4656.2.13.

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Ticks (Ixodida) are haematophagous arthropods that transmit a number of pathogenic organisms, including bacteria, protozoa and viruses, to humans and animals. Globally, there are over 900 species of ticks and Australia has 73 described species, including five introduced and 68 native species. With the exception of only a few Australian tick species, there are still many unanswered questions regarding their taxonomy and systematics, and the phylogeny of Australian ticks is not properly resolved. In recent years, a putative link between tick bites and poorly defined tick-borne illness(es) has been identified (Graves & Stenos 2017) and was the subject of a 2015 Australian Senate Inquiry into Lyme-like illnesses in Australia. There is an urgent need to further categorise Australian ticks, specifically hard ticks (Ixodidae), and accurate identification of Australian ticks is therefore of high importance.

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Wang, Yi-Shu, Rong Chen, Du-Ting Jin, Yan-Li Che, and Zong-Qing Wang. "New record of Cyrtonotula Uvarov, 1939 (Blaberidae, Epilamprinae) from China, with three new species based on morphological and COI data." ZooKeys 1021 (March 2, 2021): 127–43. http://dx.doi.org/10.3897/zookeys.1021.59526.

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The genus Cyrtonotula Uvarov, 1939 (Blaberidae, Epilamprinae) is recorded for the first time from Hainan Island, China. Three new species, Cyrtonotula epunctata Wang & Wang, sp. nov., C. maculosa Wang & Wang, sp. nov., and C. longialata Wang & Wang, sp. nov., are described based on morphological data and a molecular analysis using Automatic Barcode Gap Discovery (ABGD). Additional barcode data of blaberid species, including these three new species, are provided to facilitate future species identification. Morphological photographs and habitat photos of these new species, as well as a key to the known species, are provided.

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Ryniecki, A., M. Gawrysiak-Witulska, and J. Wawrzyniak. "Correlation for the automatic identification of drying endpoint in near-ambient dryers: Application to malting barley." Biosystems Engineering 98, no. 4 (December 2007): 437–45. http://dx.doi.org/10.1016/j.biosystemseng.2007.09.014.

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Xiong, Xingguo, Mingzhou Lu, Weizhong Yang, Guanghui Duan, Qingyan Yuan, Mingxia Shen, Tomas Norton, and Daniel Berckmans. "An Automatic Head Surface Temperature Extraction Method for Top-View Thermal Image with Individual Broiler." Sensors 19, no. 23 (November 30, 2019): 5286. http://dx.doi.org/10.3390/s19235286.

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Surface temperature variation in a broiler’s head can be used as an indicator of its health status. Surface temperatures in the existing thermograph based animal health assessment studies were mostly obtained manually. 2185 thermal images, each of which had an individual broiler, were captured from 20 broilers. Where 15 broilers served as the experimental group, they were injected with 0.1mL of pasteurella inoculum. The rest, 5 broilers, served as the control group. An algorithm was developed to extract head surface temperature automatically from the top-view broiler thermal image. Adaptive K-means clustering and ellipse fitting were applied to locate the broiler’s head region. The maximum temperature inside the head region was extracted as the head surface temperature. The developed algorithm was tested in Matlab® (R2016a) and the testing results indicated that the head region in 92.77% of the broiler thermal images could be located correctly. The maximum error of the extracted head surface temperatures was not greater than 0.1 °C. Different trend features were observed in the smoothed head surface temperature time series of the broilers in experimental and control groups. Head surface temperature extracted by the presented algorithm lays a foundation for the development of an automatic system for febrile broiler identification.

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Pan, Yuanzhi, Hua Jin, Jiechao Gao, and Hafiz Tayyab Rauf. "Identification of Buffalo Breeds Using Self-Activated-Based Improved Convolutional Neural Networks." Agriculture 12, no. 9 (September 3, 2022): 1386. http://dx.doi.org/10.3390/agriculture12091386.

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The livestock of Pakistan includes different animal breeds utilized for milk farming and exporting worldwide. Buffalo have a high milk production rate, and Pakistan is the third-largest milk-producing country, and its production is increasing over time. Hence, it is essential to recognize the best Buffalo breed for a high milk- and meat yield to meet the world’s demands and breed production. Pakistan has the second-largest number of buffalos among countries worldwide, where the Neli-Ravi breed is the most common. The extensive demand for Neli and Ravi breeds resulted in the new cross-breed “Neli-Ravi” in the 1960s. Identifying and segregating the Neli-Ravi breed from other buffalo breeds is the most crucial concern for Pakistan’s dairy-production centers. Therefore, the automatic detection and classification of buffalo breeds are required. In this research, a computer-vision-based recognition framework is proposed to identify and classify the Neli-Ravi breed from other buffalo breeds. The proposed framework employs self-activated-based improved convolutional neural networks (CNN) combined with self-transfer learning. Moreover, feature maps extracted from CNN are further transferred to obtain rich feature vectors. Different machine learning (Ml) classifiers are adopted to classify the feature vectors. The proposed framework is evaluated on two buffalo breeds, namely, Neli-Ravi and Khundi, and one additional target class contains different buffalo breeds collectively called Mix. The proposed research achieves a maximum of 93% accuracy using SVM and more than 85% accuracy employing recent variants.

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Lessire, Françoise, Nassim Moula, Jean-Luc Hornick, and Isabelle Dufrasne. "Systematic Review and Meta-Analysis: Identification of Factors Influencing Milking Frequency of Cows in Automatic Milking Systems Combined with Grazing." Animals 10, no. 5 (May 25, 2020): 913. http://dx.doi.org/10.3390/ani10050913.

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More dairy farms (up to more than one in four in some countries) are equipped with automatic milking systems (AMS) worldwide. Because of the positive impacts of grazing, e.g., on animal welfare or on production costs, numerous researchers have published papers on the combination of AMS with grazing. However, pasture-based AMS usually causes a reduction in milking frequency (MF) compared to indoors systems. The objectives of this meta-analysis were to review publications on the impacts of pasture-based AMS on MF and mitigation strategies. First, data from 43 selected studies were gathered in a dataset including 14 parameters, and on which a Principal Component Analysis (PCA) was performed, leading to the description of four clusters summarizing different management practices. Multiple pairwise comparisons were performed to determine the relationship between the highlighted parameters of MF on milk yield (MY). From these different analyses, the relationship between MF and MY was confirmed, the systems, i.e., Clusters 1 and 2, that experienced the lowest MF also demonstrated the lowest MY/cow per day. In these clusters, grazed grass was an essential component of the cow’s diet and low feeding costs compensated MY reduction. The management options described in Clusters 3 and 4 allowed maintenance of MF and MY by complementing the cows’ diets with concentrates or partial mixed ration supplied at the AMS feeding bin or provided at barn. The chosen management options were closely linked to the geographical origin of the papers indicating that other factors (e.g., climatic conditions or available grasslands) could be decisional key points for AMS management strategies.

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Silva Ramos, Aline, Cristiano Hora Fontes, Adonias Magdiel Ferreira, Camila Costa Baccili, Karen Nascimento da Silva, Viviani Gomes, and Gabriel Jesus Alves de Melo. "Somatic cell count in buffalo milk using fuzzy clustering and image processing techniques." Journal of Dairy Research 88, no. 1 (February 2021): 69–72. http://dx.doi.org/10.1017/s0022029921000042.

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AbstractThis research communication presents an automatic method for the counting of somatic cells in buffalo milk, which includes the application of a fuzzy clustering method and image processing techniques (somatic cell count with fuzzy clustering and image processing|, SCCFCI). Somatic cell count (SCC) in milk is the main biomarker for assessing milk quality and it is traditionally performed by exhaustive methods consisting of the visual observation of cells in milk smears through a microscope, which generates uncertainties associated with human interpretation. Unlike other similar works, the proposed method applies the Fuzzy C-Means (FCM) method as a preprocessing step in order to separate the images (objects) of the cells into clusters according to the color intensity. This contributes signficantly to the performance of the subsequent processing steps (thresholding, segmentation and recognition/identification). Two methods of thresholding were evaluated and the Watershed Transform was used for the identification and separation of nearby cells. A detailed statistical analysis of the results showed that the SCCFCI method is able to provide results which are consistent with those obtained by conventional counting. This method therefore represents a viable alternative for quality control in buffalo milk production.

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Kastrikin, V. A., S. A. Podol'skii, and M. S. Babykina. "A New Method for Calculating the Population Density of Terrestrial Animals Using Camera Traps with Assessment of Roe Deer (Capreolus pygargus Pallas, 1771) (Cervidae, Mammalia) Population Density in the Khingan Nature Reserve as an Example." Povolzhskiy Journal of Ecology, no. 3 (November 19, 2020): 307–17. http://dx.doi.org/10.35885/1684-7318-2020-3-307-317.

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A new method for calculating the population density of terrestrial animals, which are not amenable to individual identification, using photos or video images obtained by automatic cameras is proposed for discussion. The method is based on the continuous registration of animals on sites formed by the detection zones of camera traps with subsequent extrapolation of the results to the entire study area. A much simpler mathematical apparatus is a significant difference between our proposed method and other methods of accounting by camera traps, which allows it to be applied by a wide range of users. Both the positional measures and the scattering measures necessary for subsequent statistical analysis are calculated quite easily. Also, one of our method’s advantages is no need to know the animal movement speed, the most difficult parameter to calculate, especially in the snowless period of the year. An example of using the bootstrap method is given for the case when the input data distribution parameters do not correspond to the normal one. Using the de Moivre–Laplace theorem, the probability that the animals resting on their beds would get into the detection zone of the camera trap matrices is estimated, which is necessary for the correct use of the proposed method. Solutions are proposed for cases when this probability is low. The problems of our proposed method and their possible solutions are described. An example of calculating the density of roe deer in the open oak forest of the Khingan Nature Reserve is given on the basis of our data obtained from four camera traps.

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Paulsen, Ingrid Marie Garfelt, Åshild Ønvik Pedersen, Richard Hann, Marie-Anne Blanchet, Isabell Eischeid, Charlotte van Hazendonk, Virve Tuulia Ravolainen, Audun Stien, and Mathilde Le Le Moullec. "How Many Reindeer? UAV Surveys as an Alternative to Helicopter or Ground Surveys for Estimating Population Abundance in Open Landscapes." Remote Sensing 15, no. 1 (December 20, 2022): 9. http://dx.doi.org/10.3390/rs15010009.

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Abstract:

Conservation of wildlife depends on precise and unbiased knowledge on the abundance and distribution of species. It is challenging to choose appropriate methods to obtain a sufficiently high detectability and spatial coverage matching the species characteristics and spatiotemporal use of the landscape. In remote regions, such as in the Arctic, monitoring efforts are often resource-intensive and there is a need for cheap and precise alternative methods. Here, we compare an uncrewed aerial vehicle (UAV; quadcopter) pilot survey of the non-gregarious Svalbard reindeer to traditional population abundance surveys from ground and helicopter to investigate whether UAVs can be an efficient alternative technology. We found that the UAV survey underestimated reindeer abundance compared to the traditional abundance surveys when used at management relevant spatial scales. Observer variation in reindeer detection on UAV imagery was influenced by the RGB greenness index and mean blue channel. In future studies, we suggest testing long-range fixed-wing UAVs to increase the sample size of reindeer and area coverage and incorporate detection probability in animal density models from UAV imagery. In addition, we encourage focus on more efficient post-processing techniques, including automatic animal object identification with machine learning and analytical methods that account for uncertainties.

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Journal articles on the topic 'Animal automatic identification'

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