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Journal articles on the topic 'Precision livestock farming'

Author: Grafiati

Published: 4 June 2021

Last updated: 21 February 2023

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1

István Komlósi. "The precision livestock farming." Acta Agraria Debreceniensis, no. 49 (November 13, 2012): 201–2. http://dx.doi.org/10.34101/actaagrar/49/2525.

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The application of information technology is the response of the livestock farming to the demand of customer, legal and economical expectations. This technology is the socalled precision livestock farming (PLF). The elements of the PLF are: continuous monitoring of inputs, animal behaviour by sensors, an algorithm which converts these signals into a figure, this figure is compared to an optimum then adjustment of the input is followed, if it is necesary.

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Berckmans, Daniel. "Precision livestock farming (PLF)." Computers and Electronics in Agriculture 62, no. 1 (June 2008): 1. http://dx.doi.org/10.1016/j.compag.2007.09.002.

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Norton, T., and D. Berckmans. "Developing precision livestock farming tools for precision dairy farming." Animal Frontiers 7, no. 1 (January 1, 2017): 18–23. http://dx.doi.org/10.2527/af.2017.0104.

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Vranken, Erik, and Dries Berckmans. "Precision livestock farming for pigs." Animal Frontiers 7, no. 1 (January 1, 2017): 32–37. http://dx.doi.org/10.2527/af.2017.0106.

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Juarez, Manuel M. "238 Linking livestock phenomics and precision livestock farming." Journal of Animal Science 98, Supplement_3 (November 2, 2020): 124. http://dx.doi.org/10.1093/jas/skaa054.212.

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Abstract Numerous pre- and post-mortem factors, such as genotype, production system, growth promotants, diet, health events, stress, slaughter age and weight, carcass chilling, and ageing time, have been shown to impact beef production and final product quality. The objective of livestock phenomics is the systematic acquisition of high dimensional phenotypic data, which requires measuring phenomes as they change in response to genetic mutation and environmental influences. Due to the decrease in costs associated to genomics technology and related fields, researchers had to face the so called “phenomic gap”, a lack of sufficient, appropriate phenotypic data. Selecting phenotypes of interests, standardizing methodologies, developing high-throughput data collection systems, systematically recording environmental factors, and integrating bioinformatics are some of the challenges when developing a livestock phenomics program. Precision livestock farming aims at applying continuous, automated real-time monitoring systems to optimize livestock management. The information collected by these systems can be used to optimize individual animal health and welfare, reproductive traits, and productivity, as well as environmental influences. This approach requires the use of novel technologies and the management of large amounts of data. Multiple technologies and sensors are already being used, or have the potential, to monitor important individual traits. These two interdisciplinary fields share multiple objectives that could lead to significant synergies. The complexity of in-farm data collection varies depending on the species and production system, with beef cattle presenting specific challenges. In addition, data collection needs to continue after slaughter, as carcass and meat quality traits are influenced by in vivo practices, determine the final profitability of the system, and need to be taken into consideration to modify management practices. Integrating livestock phenomics and precision livestock farming approaches will lead to a faster development of both fields and an optimal use of resources.

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Werkheiser, Ian. "Precision Livestock Farming and Farmers’ Duties to Livestock." Journal of Agricultural and Environmental Ethics 31, no. 2 (February 16, 2018): 181–95. http://dx.doi.org/10.1007/s10806-018-9720-0.

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Berckmans, D. "General introduction to precision livestock farming." Animal Frontiers 7, no. 1 (January 1, 2017): 6–11. http://dx.doi.org/10.2527/af.2017.0102.

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Xin, Hongwei, and Kai Liu. "Precision livestock farming in egg production." Animal Frontiers 7, no. 1 (January 1, 2017): 24–31. http://dx.doi.org/10.2527/af.2017.0105.

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Berckmans, Daniel. "Bright Farm by Precision Livestock Farming." Impact 2017, no. 1 (January 9, 2017): 4–6. http://dx.doi.org/10.21820/23987073.2017.1.4.

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Norton, Tomas, and Daniel Berckmans. "Engineering advances in Precision Livestock Farming." Biosystems Engineering 173 (September 2018): 1–3. http://dx.doi.org/10.1016/j.biosystemseng.2018.09.008.

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Aquilani, C., A. Confessore, R. Bozzi, F. Sirtori, and C. Pugliese. "Review: Precision Livestock Farming technologies in pasture-based livestock systems." Animal 16, no. 1 (January 2022): 100429. http://dx.doi.org/10.1016/j.animal.2021.100429.

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Tullo, Emanuela, Alberto Finzi, and Marcella Guarino. "Review: Environmental impact of livestock farming and Precision Livestock Farming as a mitigation strategy." Science of The Total Environment 650 (February 2019): 2751–60. http://dx.doi.org/10.1016/j.scitotenv.2018.10.018.

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BERCKMANS, D. "Precision livestock farming technologies for welfare management in intensive livestock systems." Revue Scientifique et Technique de l'OIE 33, no. 1 (April 1, 2014): 189–96. http://dx.doi.org/10.20506/rst.33.1.2273.

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Berckmans, D., and M. Guarino. "From the Editors: Precision livestock farming for the global livestock sector." Animal Frontiers 7, no. 1 (January 1, 2017): 4–5. http://dx.doi.org/10.2527/af.2017.0101.

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Hartung, Jörg, Thomas Banhazi, Erik Vranken, and Marcella Guarino. "European farmers' experiences with precision livestock farming systems." Animal Frontiers 7, no. 1 (January 1, 2017): 38–44. http://dx.doi.org/10.2527/af.2017.0107.

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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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Vaghamashi, G., G. P. Sabapara, D. D. Garg, H. H. Savsani, M. R. Chavda, A. Chavda, N. K. Ribadiya, V. K. Karangiya, and R. B. Makwana. "Precision Dairy Farming: The New Era in Dairy Farming." International Journal of Current Microbiology and Applied Sciences 11, no. 5 (May 10, 2022): 20–28. http://dx.doi.org/10.20546/ijcmas.2022.1105.004.

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India has world’s largest livestock population and 1st rank in milk production with 187 million tons in 2018-19. Dairy farming is the single largest contributors to Indian GDP with 5.1 % in 2018-19 and annual growth rate in Livestock sector is 4.6%. Indian dairy production is characterized as a low input/low output system. In general, milk productivity of dairy animals is very low in comparison to global standards. The lower milk yield is mainly due to low genetic potential, lack of nutritional feeds and inadequate veterinary services. So, with suitable and scientific approach production efficiency can raise. In this context, Precision dairy farming (PDF) aims to improve individual animal performance, well being and socio-economic status of dairy farmer. Today, traditional dairy farming becomes organized commercial business with technological specializations in every part of the process. Thus, farmers are shifting towards adopting modern dairy farming practices for increase their production. PDF is the use of information and technology based farm management system to record physiological, behavioral and production parameters of individual animals to improve management strategies, profitability and production performance. There are so many important precision dairy farming technologies available globally, which are routinely useful for large and commercial dairy farm. In this direction, the authors have also highlighted the status of adoption in Indian scenario, benefits, challenges and limitations of precision dairy farming technologies. Many developing countries including India are in initial stage in these advance technology, but there are tremendous opportunities for betterment of animal and upliftment of animal husbandry profession.

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Gailums, A. "Precision Agriculture in Latvia in the Last 20 Years." Advances in Animal Biosciences 8, no. 2 (June 1, 2017): 698–702. http://dx.doi.org/10.1017/s2040470017000681.

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The establishment of the system Soil – Yield that occured in Latvia during the 1970–80s could be considered as the beginning of precision farming with the available technologies. The first precision farming technologies have been associated with harvesting where combines and tractors with the automatic steering were used. The precision agriculture in Latvia includes various branches. Latvia farmers are using precision crop farming, precision livestock farming, precision fruit growing, precision bee keeping, precision farming greenhouse and precision growing berries. Precision farming technologies in Latvia are introduced mainly in large scale farms, with more than 1000 ha. The most important researches in precision farming in Latvia were done in 2000s.

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Monteiro, António, Sérgio Santos, and Pedro Gonçalves. "Precision Agriculture for Crop and Livestock Farming—Brief Review." Animals 11, no. 8 (August 9, 2021): 2345. http://dx.doi.org/10.3390/ani11082345.

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In the last few decades, agriculture has played an important role in the worldwide economy. The need to produce more food for a rapidly growing population is creating pressure on crop and animal production and a negative impact to the environment. On the other hand, smart farming technologies are becoming increasingly common in modern agriculture to assist in optimizing agricultural and livestock production and minimizing the wastes and costs. Precision agriculture (PA) is a technology-enabled, data-driven approach to farming management that observes, measures, and analyzes the needs of individual fields and crops. Precision livestock farming (PLF), relying on the automatic monitoring of individual animals, is used for animal growth, milk production, and the detection of diseases as well as to monitor animal behavior and their physical environment, among others. This study aims to briefly review recent scientific and technological trends in PA and their application in crop and livestock farming, serving as a simple research guide for the researcher and farmer in the application of technology to agriculture. The development and operation of PA applications involve several steps and techniques that need to be investigated further to make the developed systems accurate and implementable in commercial environments.

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Negrete, Jaime Cuauhtemoc. "Mechatronics and Precision Livestock Farming in Mexican Animal Production." Animal Review 4, no. 1 (2017): 1–7. http://dx.doi.org/10.18488/journal.ar.2017.41.1.7.

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Benjamin, Madonna, and Steven Yik. "Precision Livestock Farming in Swine Welfare: A Review for Swine Practitioners." Animals 9, no. 4 (March 31, 2019): 133. http://dx.doi.org/10.3390/ani9040133.

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The burgeoning research and applications of technological advances are launching the development of precision livestock farming. Through sensors (cameras, microphones and accelerometers), images, sounds and movements are combined with algorithms to non-invasively monitor animals to detect their welfare and predict productivity. In turn, this remote monitoring of livestock can provide quantitative and early alerts to situations of poor welfare requiring the stockperson’s attention. While swine practitioners’ skills include translation of pig data entry into pig health and well-being indices, many do not yet have enough familiarity to advise their clients on the adoption of precision livestock farming practices. This review, intended for swine veterinarians and specialists, (1) includes an introduction to algorithms and machine learning, (2) summarizes current literature on relevant sensors and sensor network systems, and drawing from industry pig welfare audit criteria, (3) explains how these applications can be used to improve swine welfare and meet current pork production stakeholder expectations. Swine practitioners, by virtue of their animal and client advocacy roles, interpretation of benchmarking data, and stewardship in regulatory and traceability programs, can play a broader role as advisors in the transfer of precision livestock farming technology, and its implications to their clients.

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Neethirajan, Suresh, and Bas Kemp. "Digital Twins in Livestock Farming." Animals 11, no. 4 (April 3, 2021): 1008. http://dx.doi.org/10.3390/ani11041008.

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Artificial intelligence (AI), machine learning (ML) and big data are consistently called upon to analyze and comprehend many facets of modern daily life. AI and ML in particular are widely used in animal husbandry to monitor both the animals and environment around the clock, which leads to a better understanding of animal behavior and distress, disease control and prevention, and effective business decisions for the farmer. One particularly promising area that advances upon AI is digital twin technology, which is currently used to improve efficiencies and reduce costs across multiple industries and sectors. In contrast to a model, a digital twin is a digital replica of a real-world entity that is kept current with a constant influx of data. The application of digital twins within the livestock farming sector is the next frontier and has the potential to be used to improve large-scale precision livestock farming practices, machinery and equipment usage, and the health and well-being of a wide variety of farm animals. The mental and emotional states of animals can be monitored using recognition technology that examines facial features, such as ear postures and eye white regions. Used with modeling, simulation and augmented reality technologies, digital twins can help farmers to build more energy-efficient housing structures, predict heat cycles for breeding, discourage negative behaviors of livestock, and potentially much more. As with all disruptive technological advances, the implementation of digital twin technology will demand a thorough cost and benefit analysis of individual farms. Our goal in this review is to assess the progress toward the use of digital twin technology in livestock farming, with the goal of revolutionizing animal husbandry in the future.

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Kleen, Joachim Lübbo. "Precision Livestock Farming – Definition und Bedeutung für Tierärztinnen und Tierärzte." veterinär spiegel 32, no. 02 (June 2022): 85–91. http://dx.doi.org/10.1055/a-1844-7441.

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Bianchi, M. C., L. Bava, A. Sandrucci, F. M. Tangorra, A. Tamburini, G. Gislon, and M. Zucali. "Diffusion of precision livestock farming technologies in dairy cattle farms." animal 16, no. 11 (November 2022): 100650. http://dx.doi.org/10.1016/j.animal.2022.100650.

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John, A. J., C. E. F. Clark, M. J. Freeman, K. L. Kerrisk, S. C. Garcia, and I. Halachmi. "Review: Milking robot utilization, a successful precision livestock farming evolution." Animal 10, no. 9 (2016): 1484–92. http://dx.doi.org/10.1017/s1751731116000495.

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Van Hertem, T., L. Rooijakkers, D. Berckmans, A. Peña Fernández, T. Norton, D. Berckmans, and E. Vranken. "Appropriate data visualisation is key to Precision Livestock Farming acceptance." Computers and Electronics in Agriculture 138 (June 2017): 1–10. http://dx.doi.org/10.1016/j.compag.2017.04.003.

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Ņikitenko, Agris, Signe Bāliņa, Andrejs Dubrovskis, Ilze Andersone, and Ilze Birzniece. "Precision Livestock Farming IT Support Model for the Poultry Industry." Complex Systems Informatics and Modeling Quarterly, no. 32 (October 28, 2022): 44–54. http://dx.doi.org/10.7250/csimq.2022-32.03.

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The presented work proposes a practical approach to bird weight data processing and augmentation to enable production outcome forecast model training, which contributes to higher productivity. We suggest using the parametrized model, where parameter values are found through genetic optimization and thus are closely corresponding to broiler body weight factual measurements. The proposed approach is implemented as a stand-alone software system, exposing the models through containerized web services enabling different use scenarios.

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di Virgilio, Agustina, Juan M. Morales, Sergio A. Lambertucci, Emily L. C. Shepard, and Rory P. Wilson. "Multi-dimensional Precision Livestock Farming: a potential toolbox for sustainable rangeland management." PeerJ 6 (May 30, 2018): e4867. http://dx.doi.org/10.7717/peerj.4867.

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Background Precision Livestock Farming (PLF) is a promising approach to minimize the conflicts between socio-economic activities and landscape conservation. However, its application on extensive systems of livestock production can be challenging. The main difficulties arise because animals graze on large natural pastures where they are exposed to competition with wild herbivores for heterogeneous and scarce resources, predation risk, adverse weather, and complex topography. Considering that the 91% of the world’s surface devoted to livestock production is composed of extensive systems (i.e., rangelands), our general aim was to develop a PLF methodology that quantifies: (i) detailed behavioural patterns, (ii) feeding rate, and (iii) costs associated with different behaviours and landscape traits. Methods For this, we used Merino sheep in Patagonian rangelands as a case study. We combined data from an animal-attached multi-sensor tag (tri-axial acceleration, tri-axial magnetometry, temperature sensor and Global Positioning System) with landscape layers from a Geographical Information System to acquire data. Then, we used high accuracy decision trees, dead reckoning methods and spatial data processing techniques to show how this combination of tools could be used to assess energy balance, predation risk and competition experienced by livestock through time and space. Results The combination of methods proposed here are a useful tool to assess livestock behaviour and the different factors that influence extensive livestock production, such as topography, environmental temperature, predation risk and competition for heterogeneous resources. We were able to quantify feeding rate continuously through time and space with high accuracy and show how it could be used to estimate animal production and the intensity of grazing on the landscape. We also assessed the effects of resource heterogeneity (inferred through search times), and the potential costs associated with predation risk, competition, thermoregulation and movement on complex topography. Discussion The quantification of feeding rate and behavioural costs provided by our approach could be used to estimate energy balance and to predict individual growth, survival and reproduction. Finally, we discussed how the information provided by this combination of methods can be used to develop wildlife-friendly strategies that also maximize animal welfare, quality and environmental sustainability.

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Vrchota, Jaroslav, Martin Pech, and Ivona Švepešová. "Precision Agriculture Technologies for Crop and Livestock Production in the Czech Republic." Agriculture 12, no. 8 (July 22, 2022): 1080. http://dx.doi.org/10.3390/agriculture12081080.

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Modern technologies are penetrating all fields of human activity, including agriculture, where they significantly affect the quantity and quality of agricultural production. Precision agriculture can be characterised as an effort to improve the results of practical farming, achieving higher profits by exploiting the existing spatial unevenness of soil properties. We aim to evaluate precision agriculture technologies’ practical use in agricultural enterprises in the Czech Republic. The research was based on a questionnaire survey in which 131 farms participated. We validated the hypothesis through a Chi-squared test on the frequency of occurrence of end-use technology. The results showed that precision farming technologies are used more in crop than livestock production. In particular, 58.02% of enterprises use intelligent weather stations, 89.31% use uncrewed vehicles, and 61.83% use navigation and optimisation systems for optimising journeys. These technologies are the most used and closely related to autonomous driving and robotics in agriculture. The results indicate how willing are agricultural enterprises to adopt new technologies. For policy makers, these findings show which precision farming technologies are already implemented. This can make it easier to direct funding towards grants and projects.

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Nastasijevic, I., I. Brankovic Lazic, and Z. Petrovic. "Precision livestock farming in the context of meat safety assurance system." IOP Conference Series: Earth and Environmental Science 333 (October 14, 2019): 012014. http://dx.doi.org/10.1088/1755-1315/333/1/012014.

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Qiao, Yongliang, Yangyang Guo, and Dongjian He. "Cattle body detection based on YOLOv5-ASFF for precision livestock farming." Computers and Electronics in Agriculture 204 (January 2023): 107579. http://dx.doi.org/10.1016/j.compag.2022.107579.

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Laca, Emilio A. "Precision livestock production: tools and concepts." Revista Brasileira de Zootecnia 38, spe (July 2009): 123–32. http://dx.doi.org/10.1590/s1516-35982009001300014.

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Precision livestock production (PLP) is the augmentation of precision agriculture (PA) concepts to include all components of agroecosystems, particularly animals and plant-animal interactions. Soil, plants and soil-plant interactions are the subjects of PA or site-specific farming, where the main principle is to exploit natural spatial heterogeneity to increase efficiency and reduce environmental impacts. For the most part, PA has been studied and developed for intensive cropping systems with little attention devoted to pastoral and agropastoral systems. PLP focuses on the animal component and exploits heterogeneity in space and among individual animals towards more efficient and environmentally friendly production. Within PLP, precision grazing consists of the integration of information and communication technologies with knowledge about animal behavior and physiology to improve production of meat, milk and wool in grazing conditions. Two main goals are to minimize overgrazing of sensitive areas and to maximize the quality of the product through enhanced traceability. An integrated precision grazing system is outlined with its components: sensors of animal position, behavior and physiological status, real-time transmission of information to a decision support system, and feed-back through a series of actuators. Control of animal movement and diets is based on knowledge about species specific responses to various stimuli within the paradigms of flavor aversions and operant conditioning. Recent advances in the technologies and instrumentation available are reviewed briefly and linked to current livestock identification systems. The precision grazing vision is presented in full and the areas that need further research and development are discussed.

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Olejnik, Katarzyna, Ewa Popiela, and Sebastian Opaliński. "Emerging Precision Management Methods in Poultry Sector." Agriculture 12, no. 5 (May 18, 2022): 718. http://dx.doi.org/10.3390/agriculture12050718.

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New approach to improve welfare in the poultry sector is targeted at the precise management of animals. In poultry production, we observe that birds’ health and quality of poultry products depend significantly on good welfare conditions, affecting economic efficiency. Using technology solutions in different systems of animal production is an innovation that can help farmers more effectively control the environmental conditions and health of birds. In addition, rising public concern about poultry breeding and welfare leads to developing solutions to increase the efficiency of control and monitoring in this animal production branch. Precision livestock farming (PLF) collects real-time data of birds using different types of technologies for this process. It means that PLF can help prevent lowering animal welfare by detecting early stages of diseases and stressful situations during birds’ management and allows steps to be taken quickly enough to limit the adverse effects. This review shows connections between the possibilities of using the latest technologies to monitor laying hens and broilers in developing precision livestock farming.

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Lippmann, Deborah. "Evonik treibt den digitalen Hühnerstall voran." agrarzeitung 76, no. 14 (2021): 6. http://dx.doi.org/10.51202/1869-9707-2021-14-006-1.

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Radun, Viktor, Dragan Dokić, and Vesna Gantner. "Implementing artificial intelligence as a part of precision dairy farming for enabling sustainable dairy farming." Ekonomika poljoprivrede 68, no. 4 (2021): 869–80. http://dx.doi.org/10.5937/ekopolj2104869r.

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The purpose of this paper is to consider implementation of Artificial Intelligence as a part of Precision Dairy Farming, as a way of processing, analyzing and managing Big data, in order to enable sustainable dairy cattle farm. Increasing the volume of livestock production in the future and measuring the level of environmental impact becomes one of the most pressing concerns. The aim is to evaluate the impact of animal's production level on the ammonium pollution from dairy cattle farm using precision dairy farming technologies. The results indicate significant variability in estimated ammonium pollution from dairy cattle farms due to the animal's production indicating positive correlation between daily milk production and ammonium pollution. The test day records, as Artificial Intelligence application in precision dairy farming could be used both for assessing the ammonium pollution from farms and timely prevention and correction of inadequate management towards sustainable dairy production systems.

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Bahlo, Christiane, Peter Dahlhaus, Helen Thompson, and Mark Trotter. "The role of interoperable data standards in precision livestock farming in extensive livestock systems: A review." Computers and Electronics in Agriculture 156 (January 2019): 459–66. http://dx.doi.org/10.1016/j.compag.2018.12.007.

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Subach, T. I., and Zh N. Shmeleva. "Introduction of digital innovations in livestock farming." IOP Conference Series: Earth and Environmental Science 1112, no. 1 (December 1, 2022): 012079. http://dx.doi.org/10.1088/1755-1315/1112/1/012079.

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Abstract Nowadays one can observe the penetration of digitalization into almost all spheres of life: political, social, economic, educational. Agriculture is not an exception. Russian government considers digital transformation in general and agriculture in particular to be one of the indispensable national goals. The ultimate goal of introducing digital changes is guaranteeing the RF entry into the top five largest economies in the world. Agriculture in RF has various branches where animal husbandry plays and important role in ensuring food security. Consequently, the issue of introducing digitalization into livestock farming as a less advanced industry is rather relevant. Digital data is becoming a key factor in the livestock industry. This data can be obtained from various devices, in particular from biometric sensors, big data and blockchain technology. Precision Livestock farming (PLF) technologies enable non-invasive sampling, helping farmers and researchers to obtain realistic indicators to solve welfare problems. The received and processed data allow obtaining information having new quality characteristics, searching for and finding patterns and models for agricultural successful modernization, minimizing risks of losses, forecasting using modern methods of big data processing, optimizing production costs. PLF technologies can also contribute to a reduction in resource use. They will definitely facilitate individualistic approach and a more proactive to animal health. Such an approach will ultimately reduce the need for medicines, especially the use of antibiotics. The use of innovative solutions was noted, in particular, the use of automated technologies, robotic systems, computerized programs, applications that contribute to the growth of productivity and the production of livestock products.

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Bello, R. W., A. S. A. Mohamed, A. Z. Talib, D. A. Olubummo, and O. C. Enuma. "Computer vision-based techniques for cow object recognition." IOP Conference Series: Earth and Environmental Science 858, no. 1 (September 1, 2021): 012008. http://dx.doi.org/10.1088/1755-1315/858/1/012008.

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Abstract The productivity of livestock farming depends on the welfare of the livestock. This can be achieved by physically and constantly monitoring their behaviors and activities by human experts. However, the degree of having high accuracy and consistency with manual monitoring in a commercial farm is herculean, and in most cases impractical. Hence, there is a need for a method that can overcome the challenges. Proposed in this paper, therefore, is the cow detection and monitoring method using computer vision techniques. The proposed method is capable of tracking and identifying cow objects in video experiments, thereby actualizing precision livestock farming. The method generates reasonable results when compared to other methods.

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Krul, Sander, Christos Pantos, Mihai Frangulea, and João Valente. "Visual SLAM for Indoor Livestock and Farming Using a Small Drone with a Monocular Camera: A Feasibility Study." Drones 5, no. 2 (May 19, 2021): 41. http://dx.doi.org/10.3390/drones5020041.

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Real-time data collection and decision making with drones will play an important role in precision livestock and farming. Drones are already being used in precision agriculture. Nevertheless, this is not the case for indoor livestock and farming environments due to several challenges and constraints. These indoor environments are limited in physical space and there is the localization problem, due to GPS unavailability. Therefore, this work aims to give a step toward the usage of drones for indoor farming and livestock management. To investigate on the drone positioning in these workspaces, two visual simultaneous localization and mapping (VSLAM)—LSD-SLAM and ORB-SLAM—algorithms were compared using a monocular camera onboard a small drone. Several experiments were carried out in a greenhouse and a dairy farm barn with the absolute trajectory and the relative pose error being analyzed. It was found that the approach that suits best these workspaces is ORB-SLAM. This algorithm was tested by performing waypoint navigation and generating maps from the clustered areas. It was shown that aerial VSLAM could be achieved within these workspaces and that plant and cattle monitoring could benefit from using affordable and off-the-shelf drone technology.

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Abeni, Fabio, Francesca Petrera, and Andrea Galli. "A Survey of Italian Dairy Farmers’ Propensity for Precision Livestock Farming Tools." Animals 9, no. 5 (April 28, 2019): 202. http://dx.doi.org/10.3390/ani9050202.

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A targeted survey was designed with the aim of describing the diffusion of precision livestock farming (PLF) tools in one of the most intensive dairy farming provinces in Italy. Technicians at the Provincial Breeder Association of Cremona interviewed 490 dairy farmers and obtained data regarding the role and age of the respondents; the land owned by the farmers; their herd sizes (HS, lactating plus dry cows; small HS < 101, medium HS 101–200, large HS > 200 cows/herd); their average 305 day milk yield (low MY < 9501, medium MY 9501–10,500, high MY > 10,500 kg/head); the cow to employed worker ratio (low CW < 33, medium CW 33–47, high CW > 47 cows/worker); the use of PLF tools to monitor production, reproduction, and health; and the criteria and motivations for investing in PLF tools. The use of automated MY recording and estrus detection systems was primarily associated with HS (more present in larger farms), followed by MY (more present in more productive farms), and then CW (more present with a high cow: worker ratio). Concern about the time required to manage data was the most common subjective issue identified as negatively affecting the purchase of these tools. The future of PLF use in this region will depend upon the availability of an effective selection of tools on the market.

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Lee, Mingyung, and Seongwon Seo. "Usage Status Survey of Precision Livestock Farming Technologies on Korean Dairy Farm." Journal of the Korea Academia-Industrial cooperation Society 23, no. 2 (February 28, 2022): 678–88. http://dx.doi.org/10.5762/kais.2022.23.2.678.

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Fote, Fabrice Nolack, Amine Roukh, Saïd Mahmoudi, Sidi Ahmed Mahmoudi, and Olivier Debauche. "Toward a Big Data Knowledge-Base Management System for Precision Livestock Farming." Procedia Computer Science 177 (2020): 136–42. http://dx.doi.org/10.1016/j.procs.2020.10.021.

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Halachmi, I., and M. Guarino. "Editorial: Precision livestock farming: a ‘per animal’ approach using advanced monitoring technologies." Animal 10, no. 9 (2016): 1482–83. http://dx.doi.org/10.1017/s1751731116001142.

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Bos, Jacqueline M., Bernice Bovenkerk, Peter H. Feindt, and Ynte K. van Dam. "The Quantified Animal: Precision Livestock Farming and the Ethical Implications of Objectification." Food Ethics 2, no. 1 (November 27, 2018): 77–92. http://dx.doi.org/10.1007/s41055-018-00029-x.

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Morrone, Sarah, Corrado Dimauro, Filippo Gambella, and Maria Grazia Cappai. "Industry 4.0 and Precision Livestock Farming (PLF): An up to Date Overview across Animal Productions." Sensors 22, no. 12 (June 7, 2022): 4319. http://dx.doi.org/10.3390/s22124319.

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Precision livestock farming (PLF) has spread to various countries worldwide since its inception in 2003, though it has yet to be widely adopted. Additionally, the advent of Industry 4.0 and the Internet of Things (IoT) have enabled a continued advancement and development of PLF. This modern technological approach to animal farming and production encompasses ethical, economic and logistical aspects. The aim of this review is to provide an overview of PLF and Industry 4.0, to identify current applications of this rather novel approach in different farming systems for food producing animals, and to present up to date knowledge on the subject. Current scientific literature regarding the spread and application of PLF and IoT shows how efficient farm animal management systems are destined to become. Everyday farming practices (feeding and production performance) coupled with continuous and real-time monitoring of animal parameters can have significant impacts on welfare and health assessment, which are current themes of public interest. In the context of feeding a rising global population, the agri-food industry and industry 4.0 technologies may represent key features for successful and sustainable development.

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Hurst, William, Frida Ruiz Mendoza, and Bedir Tekinerdogan. "Augmented Reality in Precision Farming: Concepts and Applications." Smart Cities 4, no. 4 (December 2, 2021): 1454–68. http://dx.doi.org/10.3390/smartcities4040077.

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The amount of arable land is limited, yet the demand for agricultural food products is increasing. This issue has led to the notion of precision farming, where smart city-based technologies (e.g., Internet of Things, digital twins, artificial intelligence) are employed in combination to cater for increased production with fewer resources. Widely used in manufacturing, augmented reality has demonstrated impactful solutions for information communication, remote monitoring and increased interaction. Yet, the technology has only recently begun to find a footing alongside precision farming solutions, despite the many benefits possible to farmers through augmenting the physical world with digital objects. Therefore, this article reflects on literature discussing current applied solutions within agriculture, where augmented realty has demonstrated a significant impact for monitoring and production. The findings discuss that augmented reality must be coupled with other technologies (e.g., simultaneous localization and mapping algorithms, global positioning systems, and sensors), specifically 9 are identified across 2 application domains (livestock and crop farming) to be beneficial. Attention is also provided on how augmented reality should be employed within agriculture, where related-work examples are drawn from in order to discuss suitable hardware approaches and constraints (e.g., mobility).

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Wang, Shunli, Honghua Jiang, Yongliang Qiao, Shuzhen Jiang, Huaiqin Lin, and Qian Sun. "The Research Progress of Vision-Based Artificial Intelligence in Smart Pig Farming." Sensors 22, no. 17 (August 30, 2022): 6541. http://dx.doi.org/10.3390/s22176541.

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Pork accounts for an important proportion of livestock products. For pig farming, a lot of manpower, material resources and time are required to monitor pig health and welfare. As the number of pigs in farming increases, the continued use of traditional monitoring methods may cause stress and harm to pigs and farmers and affect pig health and welfare as well as farming economic output. In addition, the application of artificial intelligence has become a core part of smart pig farming. The precision pig farming system uses sensors such as cameras and radio frequency identification to monitor biometric information such as pig sound and pig behavior in real-time and convert them into key indicators of pig health and welfare. By analyzing the key indicators, problems in pig health and welfare can be detected early, and timely intervention and treatment can be provided, which helps to improve the production and economic efficiency of pig farming. This paper studies more than 150 papers on precision pig farming and summarizes and evaluates the application of artificial intelligence technologies to pig detection, tracking, behavior recognition and sound recognition. Finally, we summarize and discuss the opportunities and challenges of precision pig farming.

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Du, Longhuan, Li Yang, Chaowu Yang, Chenming Hu, Chunlin Yu, Mohan Qiu, Siyang Liu, Shiliang Zhu, and Xianlin Ye. "Development and Validation of an Energy Consumption Model for Animal Houses Achieving Precision Livestock Farming." Animals 12, no. 19 (September 27, 2022): 2580. http://dx.doi.org/10.3390/ani12192580.

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Indoor environmental control is usually applied in poultry farming to ensure optimum growth conditions for birds. However, these control methods represent a considerable share of total energy consumption, and the trend of applying new equipment in the future for precision livestock farming would further increase energy demand, resulting in an increase in greenhouse gas emissions and management costs. Therefore, to ensure optimum efficiency of both energy use and livestock productivity, a customized hourly model was developed in the present study to interpret and analyze the electronically collected data. The modules for estimating indoor gas concentrations were incorporated into the present model, as this has not been properly considered in previous studies. A validation test was performed in a manure-belt layer house using sensors and meters to measure the indoor environmental parameters and energy consumption. The predicted results, including indoor temperature, relative humidity, carbon dioxide and ammonia concentrations, showed good agreement with the measured data, indicating a similar overall trend with acceptable discrepancies. Moreover, the corresponding differences between the measured and simulated energy consumption for heating, tunnel ventilation and base ventilation were 13.7, 7.5, and 0.1%, respectively. The total energy demand estimated by the model showed a limited discrepancy of approximately 10.6% compared with that measured in reality. Although human factors, including inspection, cleaning, vaccination, etc., were not included in the model, the validation results still suggested that the customized model was able to accurately predict the indoor environment and overall energy consumption during poultry farming. The validated model provides a tool for poultry producers to optimize production planning and management strategies, increase the production rate of unit energy consumption and achieve precision livestock farming from an energy consumption standpoint.

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Caria, Maria, Giuseppe Todde, Gabriele Sara, Marco Piras, and Antonio Pazzona. "Performance and Usability of Smartglasses for Augmented Reality in Precision Livestock Farming Operations." Applied Sciences 10, no. 7 (March 28, 2020): 2318. http://dx.doi.org/10.3390/app10072318.

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In recent years, smartglasses for augmented reality are becoming increasingly popular in professional contexts. However, no commercial solutions are available for the agricultural field, despite the potential of this technology to help farmers. Many head-wearable devices in development possess a variety of features that may affect the smartglasses wearing experience. Over the last decades, dairy farms have adopted new technologies to improve their productivity and profit. However, there remains a gap in the literature as regards the application of augmented reality in livestock farms. Head-wearable devices may offer invaluable benefits to farmers, allowing real-time information monitoring of each animal during on-farm activities. The aim of this study was to expand the knowledge base on how augmented reality devices (smartglasses) interact with farming environments, focusing primarily on human perception and usability. Research has been conducted examining the GlassUp F4 smartglasses during animal selection process. Sixteen participants performed the identification and grouping trials in the milking parlor, reading different types of contents on the augmented reality device optical display. Two questionnaires were used to evaluate the perceived workload and usability of the device. Results showed that the information type could influence the perceived workload and the animal identification process. Smart glasses for augmented reality were a useful tool in the animal genetic improvement program offering promising opportunities for adoption in livestock operations in terms of assessing data consultation and information about animals.

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Krampe, Caspar, Jordi Serratosa, Jarkko K. Niemi, and Paul T. M. Ingenbleek. "Consumer Perceptions of Precision Livestock Farming—A Qualitative Study in Three European Countries." Animals 11, no. 5 (April 23, 2021): 1221. http://dx.doi.org/10.3390/ani11051221.

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Scholars in the fields of animal science and technology have investigated how precision livestock farming (PLF) can contribute to the quality and efficiency of animal husbandry and to the health and welfare of farm animals. Although the results of such studies provide promising avenues for the development of PLF technologies and their potential for the application in animal husbandry, the perspectives of consumers with regard to PLF technologies have yet to be the subject of much investigation. To address this research gap, the current study explores consumer perceptions of PLF technologies within the pork and dairy value chains. The investigation is based on results from six focus group discussions conducted in three European countries, each reflecting a different market environment: Finland, the Netherlands and Spain. The results indicate that consumers expect the implementation of different PLF technologies to enhance the health and welfare of farm animals, while generating environmental improvements and increasing the transparency of value-chain processes. The analysis further reveals three over-arching consumer concerns: (1) the fear that the integration of PLF technologies will introduce more industrialisation into livestock farming production; (2) the concern that PLF technologies and data are vulnerable to misuse and cyber-crime; and (3) the concern that PLF information is not communicated adequately to allow informed purchase decisions. The research findings provide directions for members of the animal-based food value chain to make informed decisions to improve their sustainability, social responsibility and credibility by endorsing the acceptance of PLF (technologies) amongst European consumers.

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