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By monitoring the behaviour of the animals, such as feeding, rumination, walking, and lying, it is possible to understand their physical and psychological status. Precision Livestock Farming (PLF) tools offer a good solution to assist the farmer in managing the herd, overcoming the limits of human control, and to react early in the case of animal health issues. The purpose of this review is to highlight a key concern that occurs in the design and validation of IoT-based systems created for monitoring grazing cows in extensive agricultural systems, since they have many more, and more complicated, problems than indoor farms. In this context, the most common concerns are related to the battery life of the devices, the sampling frequency to be used for data collection, the need for adequate service connection coverage and transmission range, the computational site, and the performance of the algorithm embedded in IoT-systems in terms of computational cost.<\/jats:p>","DOI":"10.3390\/s23083828","type":"journal-article","created":{"date-parts":[[2023,4,10]],"date-time":"2023-04-10T03:24:18Z","timestamp":1681097058000},"page":"3828","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Cow Behavioural Activities in Extensive Farms: Challenges of Adopting Automatic Monitoring Systems"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5044-2905","authenticated-orcid":false,"given":"Dominga","family":"Mancuso","sequence":"first","affiliation":[{"name":"Department of Agriculture, Food and Environment (Di3A), Building and Land Engineering Section, University of Catania, Via S. Sofia 100, 95123 Catania, Italy"}]},{"given":"Giulia","family":"Castagnolo","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Computer Engineering (DIEEI), University of Catania, Viale A. Doria 6, 95125 Catania, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8303-3486","authenticated-orcid":false,"given":"Simona M. C.","family":"Porto","sequence":"additional","affiliation":[{"name":"Department of Agriculture, Food and Environment (Di3A), Building and Land Engineering Section, University of Catania, Via S. Sofia 100, 95123 Catania, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Clark, B., Panzone, L.A., Stewart, G.B., Kyriazakis, I., Niemi, J.K., Latvala, T., Tranter, R., Jones, P., and Frewer, L.J. (2019). Consumer Attitudes towards Production Diseases in Intensive Production Systems. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0210432"},{"key":"ref_2","unstructured":"(2023, April 05). Brusselles: European Commision Attitudes of EU Citizens towards Animal Welfare, Report; Special Eurobarometer 442; 2016. Available online: https:\/\/europa.eu\/eurobarometer\/surveys\/detail\/2096."},{"key":"ref_3","unstructured":"(2023, April 05). European Commission Online Consultation on the Future of Europe; Second Interim Report; 2019. Available online: https:\/\/commission.europa.eu\/about-european-commission\/get-involved\/past-initiatives\/citizens-dialogues\/list-citizens-dialogues-events-2015-2019\/progress-reports-citizens-dialogues_en."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"104961","DOI":"10.1016\/j.compag.2019.104961","article-title":"Evaluation of Pre-Processing Methods for the Prediction of Cattle Behaviour from Accelerometer Data","volume":"165","author":"Riaboff","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6764","DOI":"10.3168\/jds.2016-10935","article-title":"A Prognostic Model to Predict the Success of Artificial Insemination in Dairy Cows Based on Readily Available Data","volume":"99","author":"Rutten","year":"2016","journal-title":"J. Dairy Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.promfg.2018.02.034","article-title":"Industry 4.0\u2014A Glimpse","volume":"20","author":"Vaidya","year":"2018","journal-title":"Procedia Manuf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"100367","DOI":"10.1016\/j.sbsr.2020.100367","article-title":"The Role of Sensors, Big Data and Machine Learning in Modern Animal Farming","volume":"29","author":"Neethirajan","year":"2020","journal-title":"Sens. Bio-Sens. Res."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Almalki, F., Soufiene, B., Alsamhi, S., and Sakli, H. (2021). A Low-Cost Platform for Environmental Smart Farming Monitoring System Based on IoT and UAVs. Sustainability, 13.","DOI":"10.3390\/su13115908"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2347","DOI":"10.1109\/COMST.2015.2444095","article-title":"Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications","volume":"17","author":"Guizani","year":"2015","journal-title":"IEEE Commun. Surv. Tutorials"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"100408","DOI":"10.1016\/j.sbsr.2021.100408","article-title":"Digital Livestock Farming","volume":"32","author":"Neethirajan","year":"2021","journal-title":"Sens. Bio-Sens. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.meatsci.2019.05.007","article-title":"Computer Vision and Remote Sensing to Assess Physiological Responses of Cattle to Pre-Slaughter Stress, and Its Impact on Beef Quality: A review","volume":"156","author":"Fuentes","year":"2019","journal-title":"Meat Sci."},{"key":"ref_12","first-page":"1","article-title":"An Animal Welfare Platform for Extensive Livestock Production Systems","volume":"2492","author":"Doulgerakis","year":"2019","journal-title":"CEUR Workshop Proc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.compag.2017.01.021","article-title":"Development of a Threshold-Based Classifier for Real-Time Recognition of Cow Feeding and Standing Behavioural Activities from Accelerometer Data","volume":"134","author":"Arcidiacono","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.compag.2013.09.013","article-title":"Estimation of Grass Intake on Pasture for Dairy Cows Using Tightly and Loosely Mounted Di- and Tri-Axial Accelerometers Combined with Bite Count","volume":"99","author":"Oudshoorn","year":"2013","journal-title":"Comput. Electron. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"105957","DOI":"10.1016\/j.compag.2020.105957","article-title":"Classifying Season Long Livestock Grazing Behavior with the Use of a Low-Cost GPS and Accelerometer","volume":"181","author":"Brennan","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","first-page":"173","article-title":"GPS Tracking Cattle as a Monitoring Tool for Conservation and Management","volume":"34","author":"Schieltz","year":"2017","journal-title":"Afr. J. Range Forage Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s10336-015-1241-2","article-title":"Gemeinsame Nahrungssuche Bei Krahenscharben: GPS Ortung Zeigt, Dass Sich Vogel Benachbarter Kolonien Nahrungsgebiete Teilen","volume":"157","author":"Evans","year":"2016","journal-title":"J. Ornithol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Rivero, M., Grau-Campanario, P., Mullan, S., Held, S., Stokes, J., Lee, M., and Cardenas, L. (2021). Factors Affecting Site Use Preference of Grazing Cattle Studied from 2000 to 2020 through GPS Tracking: A Review. Sensors, 21.","DOI":"10.3390\/s21082696"},{"key":"ref_19","first-page":"30","article-title":"Il punto sulla Plf: Passato, presente e futuro","volume":"13","author":"Fontana","year":"2015","journal-title":"Inf. Zootec."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.compag.2008.05.014","article-title":"Automatic Real-Time Monitoring of Locomotion and Posture Behaviour of Pregnant Cows Prior to Calving Using Online Image Analysis","volume":"64","author":"Cangar","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1080\/00071660903460637","article-title":"The Relationship between Physical Activity and Leg Health in the Broiler Chicken","volume":"51","author":"Sherlock","year":"2010","journal-title":"Br. Poult. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.3168\/jds.2011-4547","article-title":"Automatic Measurement of Touch and Release Angles of the Fetlock Joint for Lameness Detection in Dairy Cattle Using Vision Techniques","volume":"95","author":"Pluk","year":"2012","journal-title":"J. Dairy Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1111\/asj.13184","article-title":"Dairy Cattle Behavior Classifications Based on Decision Tree Learning Using 3-Axis Neck-Mounted Accelerometers","volume":"90","author":"Tamura","year":"2019","journal-title":"Anim. Sci. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"105179","DOI":"10.1016\/j.compag.2019.105179","article-title":"Development of a Methodological Framework for a Robust Prediction of the Main Behaviours of Dairy Cows Using a Combination of Machine Learning Algorithms on Accelerometer Data","volume":"169","author":"Riaboff","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"105178","DOI":"10.1016\/j.compag.2019.105178","article-title":"Dam Behavior Patterns in Japanese Black Beef Cattle Prior to Calving: Automated Detection Using LSTM-RNN","volume":"169","author":"Peng","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"113271","DOI":"10.1016\/j.sna.2021.113271","article-title":"MOOnitor: An IoT Based Multi-Sensory Intelligent Device for Cattle Activity Monitoring","volume":"333","author":"Dutta","year":"2021","journal-title":"Sens. Actuators A Phys."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"106045","DOI":"10.1016\/j.compag.2021.106045","article-title":"In-Situ Classification of Cattle Behavior Using Accelerometry Data","volume":"183","author":"Arablouei","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Marcos, J.T.C., and Utete, S.W. (2021, January 1\u20134). Animal Tracking within a Formation of Drones. Proceedings of the 2021 IEEE 24th International Conference on Information Fusion (FUSION), Sun City, South Africa.","DOI":"10.23919\/FUSION49465.2021.9626844"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhou, M., Elmore, J.A., Samiappan, S., Evans, K.O., Pfeiffer, M.B., Blackwell, B.F., and Iglay, R.B. (2021). Improving Animal Monitoring Using Small Unmanned Aircraft Systems (SUAS) and Deep Learning Networks. Sensors, 21.","DOI":"10.3390\/s21175697"},{"key":"ref_30","unstructured":"Bonfanti, M., Castagnolo, G., and Arcidiacono, C. (September, January 29). Preliminary Outcomes of a Low-Power Cow Oestrus Detection System in Dairy Farms. Proceedings of the 10th European Conference on Precision Livestock Farming, Vienna, Austria."},{"key":"ref_31","first-page":"1323","article-title":"Kernel Density Estimation Analyses Based on a Low Power-Global Positioning System for Monitoring Environmental Issues of Grazing Cattle","volume":"53","author":"Porto","year":"2022","journal-title":"J. Agric. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hassan-V\u00e1squez, J.A., Maroto-Molina, F., and Guerrero-Ginel, J.E. (2022). GPS Tracking to Monitor the Spatiotemporal Dynamics of Cattle Behavior and Their Relationship with Feces Distribution. Animals, 12.","DOI":"10.3390\/ani12182383"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.rala.2020.04.001","article-title":"A GPS-based Evaluation of Factors Commonly Used to Adjust Cattle Stocking Rates on Both Extensive and Mountainous Rangelands","volume":"42","author":"Millward","year":"2020","journal-title":"Rangelands"},{"key":"ref_34","first-page":"10","article-title":"An Approach for Setting the Stocking Rate","volume":"10","author":"Holechek","year":"1988","journal-title":"Rangel. Arch."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"105491","DOI":"10.1016\/j.applanim.2021.105491","article-title":"Use of an Ear-Tag Accelerometer and a Radio-Frequency Identification (RFID) System for Monitoring the Licking Behaviour in Grazing Cattle","volume":"244","author":"Simanungkalit","year":"2021","journal-title":"Appl. Anim. Behav. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.applanim.2018.12.003","article-title":"Classification of Ingestive-Related Cow Behaviours Using RumiWatch Halter and Neck-Mounted Accelerometers","volume":"211","author":"Benaissa","year":"2018","journal-title":"Appl. Anim. Behav. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.compag.2018.12.023","article-title":"Classification of Multiple Cattle Behavior Patterns Using a Recurrent Neural Network with Long Short-Term Memory and Inertial Measurement Units","volume":"157","author":"Peng","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.compag.2016.10.006","article-title":"Behavior Classification of Cows Fitted with Motion Collars: Decomposing Multi-Class Classification into a Set of Binary Problems","volume":"131","author":"Smith","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rama.2015.02.001","article-title":"Genetic Influences on Cattle Grazing Distribution: Association of Genetic Markers with Terrain Use in Cattle","volume":"68","author":"Bailey","year":"2015","journal-title":"Rangel. Ecol. Manag."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Riaboff, L., Couvreur, S., Madouasse, A., Roig-Pons, M., Aubin, S., Massabie, P., Chauvin, A., B\u00e9d\u00e8re, N., and Plantier, G. (2020). Use of Predicted Behavior from Accelerometer Data Combined with GPS Data to Explore the Relationship between Dairy Cow Behavior and Pasture Characteristics. Sensors, 20.","DOI":"10.3390\/s20174741"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.compag.2014.12.002","article-title":"Dynamic Cattle Behavioural Classification Using Supervised Ensemble Classifiers","volume":"111","author":"Dutta","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1071\/RJ17092","article-title":"Effect of GPS Sample Interval and Paddock Size on Estimates of Distance Travelled by Grazing Cattle in Rangeland, Australia","volume":"40","author":"McGavin","year":"2018","journal-title":"Rangel. J."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e10","DOI":"10.4081\/jae.2013.201","article-title":"Design and Testing of a GPS\/GSM Collar Prototype to Combat Cattle Rustling","volume":"44","author":"Tangorra","year":"2013","journal-title":"J. Agric. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Maroto-Molina, F., Navarro-Garc\u00eda, J., Pr\u00edncipe-Aguirre, K., G\u00f3mez-Maqueda, I., Guerrero-Ginel, J.E., Garrido-Varo, A., and P\u00e9rez-Mar\u00edn, D.C. (2019). A Low-Cost IoT-Based System to Monitor the Location of a Whole Herd. Sensors, 19.","DOI":"10.3390\/s19102298"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.compag.2017.05.020","article-title":"Development of an Open-Source Algorithm Based on Inertial Measurement Units (IMU) of a Smartphone to Detect Cattle Grass Intake and Ruminating Behaviors","volume":"139","author":"Andriamandroso","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Porto, S.M.C., Giulia, C., Massimo, M., Dominga, M., and Giovanni, C. (2022). On the Determination of Acceleration Thresholds for the Automatic Detection of Cow Behavioural Activities in Extensive Livestock Systems, Springer International Publishing.","DOI":"10.1007\/978-3-030-98092-4_12"},{"key":"ref_47","unstructured":"Castagnolo, G., Mancuso, D., Palazzo, S., Spampinato, C., and Porto, S.M.C. (September, January 29). Cow Behavioural Activities Classification by Convolutional Neural Networks. Proceedings of the 10th European Conference on Precision Livestock Farming, Vienna, Austria."},{"key":"ref_48","first-page":"91","article-title":"Behavioral Classification of Data from Collars Containing Motion Sensors in Grazing Cattle","volume":"110","author":"Handcock","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Cabezas, J., Yubero, R., Visitaci\u00f3n, B., Navarro-Garc\u00eda, J., Algar, M.J., Cano, E.L., and Ortega, F. (2022). Analysis of Accelerometer and GPS Data for Cattle Behaviour Identification and Anomalous Events Detection. Entropy, 24.","DOI":"10.3390\/e24030336"},{"key":"ref_50","unstructured":"Castagnolo, G., Mancuso, D., Valenti, F., Porto, S.M.C., and Cascone, G. (2022, January 19\u201322). IoT Technologies for Herd Management. Proceedings of the 12th International AIIA Conference, Palermo, Italy."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Xu, H., Li, S., Lee, C., Ni, W., Abbott, D., Johnson, M., Lea, J.M., Yuan, J., and Campbell, D.L.M. (2020). Analysis of Cattle Social Transitional Behaviour: Attraction and Repulsion. Sensors, 20.","DOI":"10.3390\/s20185340"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"106610","DOI":"10.1016\/j.compag.2021.106610","article-title":"Predicting Livestock Behaviour Using Accelerometers: A systematic Review of Processing Techniques for Ruminant Behaviour Prediction from Raw Accelerometer Data","volume":"192","author":"Riaboff","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"105498","DOI":"10.1016\/j.compag.2020.105498","article-title":"Moving Mean-Based Algorithm for Dairy Cow\u2019s Oestrus Detection from Uniaxial-Accelerometer Data Acquired in a Free-Stall Barn","volume":"175","author":"Arcidiacono","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"54","DOI":"10.4081\/jae.2016.498","article-title":"Monitoring Feeding Behaviour of Dairy Cows Using Accelerometers","volume":"47","author":"Mattachini","year":"2016","journal-title":"J. Agric. Eng."},{"key":"ref_55","first-page":"427","article-title":"Automatic Recognition of Ingestive-Related Behaviors of Dairy Cows Based on Triaxial Acceleration","volume":"7","author":"Shen","year":"2019","journal-title":"Inf. Process. Agric."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"109497","DOI":"10.1109\/ACCESS.2021.3099212","article-title":"Real-Time Behavioral Recognition in Dairy Cows Based on Geomagnetism and Acceleration Information","volume":"9","author":"Tian","year":"2021","journal-title":"IEEE Access"},{"key":"ref_57","unstructured":"Hou, S., Cheng, X., Shi, L., and Zhang, S. (2020). Proceedings of the ACM International Conference Proceeding Series, Association for Computing Machinery."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"7180","DOI":"10.3168\/jds.2019-17611","article-title":"Short Communication: Detection of Mastication Speed during Rumination in Cattle Using 3-Axis, Neck-Mounted Accelerometers and Fast Fourier Transfer Algorithm","volume":"103","author":"Tamura","year":"2020","journal-title":"J. Dairy Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.rvsc.2017.10.005","article-title":"On the Use of On-Cow Accelerometers for the Classification of Behaviours in Dairy Barns","volume":"125","author":"Benaissa","year":"2019","journal-title":"Res. Vet. Sci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"106016","DOI":"10.1016\/j.compag.2021.106016","article-title":"Using a CNN-LSTM for Basic Behaviors Detection of a Single Dairy Cow in a Complex Environment","volume":"182","author":"Wu","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.animal.2021.100429","article-title":"Review: Precision Livestock Farming technologies in pasture-based livestock system","volume":"16","author":"Aquilani","year":"2022","journal-title":"Animal"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Herlin, A., Brunberg, E., Hultgren, J., H\u00f6gberg, N., Rydberg, A., and Skarin, A. (2021). Animal Welfare Implications of Digital Tools for Monitoring and Management of Cattle and Sheep on Pasture. Animals, 11.","DOI":"10.3390\/ani11030829"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.prevetmed.2013.04.007","article-title":"Feasibility Study on the Spatial and Temporal Movement of Samburu\u2019s Cattle and Wildlife in Kenya Using GPS Radio-Tracking, Remote Sensing and GIS","volume":"111","author":"Raizman","year":"2013","journal-title":"Prev. Vet. Med."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"77454","DOI":"10.1109\/ACCESS.2018.2883151","article-title":"Low Power Wide Area Networks: A Survey of Enabling Technologies, Applications and Interoperability Needs","volume":"6","author":"Qadir","year":"2018","journal-title":"IEEE Access"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Gomez, C., Veras, J.C., Vidal, R., Casals, L., and Paradells, J. (2019). A Sigfox Energy Consumption Model. Sensors, 19.","DOI":"10.3390\/s19030681"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.icte.2017.12.005","article-title":"A comparative study of LPWAN Technologies for Large-Scale IoT Deployment","volume":"5","author":"Mekki","year":"2019","journal-title":"ICT Express"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.biosystemseng.2006.06.012","article-title":"The Use of Global Positioning and Geographical Information Systems in the Management of Extensive Cattle Grazing","volume":"95","author":"Barbari","year":"2006","journal-title":"Biosyst. Eng."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Navarro, J., de Diego, I.M., Fern\u00e1ndez-Isabel, A., and Ortega, F. (2019, January 10\u201312). Fusion of GPS and Accelerometer Information for Anomalous Trajectories Detection. Proceedings of the ACM International Conference Proceeding Series, Vienna, Austria.","DOI":"10.1145\/3312714.3312719"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/8\/3828\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:12:35Z","timestamp":1760123555000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/8\/3828"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,8]]},"references-count":68,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["s23083828"],"URL":"https:\/\/doi.org\/10.3390\/s23083828","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,8]]}}}