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Big Data Privacy in Smart Farming: A Review. SUSTAINABILITY 2022. [DOI: 10.3390/su14159120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Smart farming aims to improve farming using modern technologies and smart devices. Smart devices help farmers to collect and analyze data regarding different aspects of their business. These data are utilized by various stakeholders, including farmers, technology providers, supply chain investigators, and agricultural service providers. These data sources can be considered big data due to their volume, velocity, and variety. The wide use of data collection and communication technologies has increased concerns about the privacy of farmers and their data. Although some previous studies have reviewed the security aspects of smart farming, the privacy challenges and solutions are not sufficiently explored in the literature. In this paper, we present a holistic review of big data privacy in smart farming. The paper utilizes a data lifecycle schema and describes privacy concerns and requirements in smart farming in each of the phases of this data lifecycle. Moreover, it provides a comprehensive review of the existing solutions and the state-of-the-art technologies that can enhance data privacy in smart farming.
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Patyk KA, McCool-Eye MJ, South DD, Burdett CL, Maroney SA, Fox A, Kuiper G, Magzamen S. Modelling the domestic poultry population in the United States: A novel approach leveraging remote sensing and synthetic data methods. GEOSPATIAL HEALTH 2020; 15. [PMID: 33461269 DOI: 10.4081/gh.2020.913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 08/27/2020] [Indexed: 06/12/2023]
Abstract
Comprehensive and spatially accurate poultry population demographic data do not currently exist in the United States; however, these data are critically needed to adequately prepare for, and efficiently respond to and manage disease outbreaks. In response to absence of these data, this study developed a national-level poultry population dataset by using a novel combination of remote sensing and probabilistic modelling methodologies. The Farm Location and Agricultural Production Simulator (FLAPS) (Burdett et al., 2015) was used to provide baseline national-scale data depicting the simulated locations and populations of individual poultry operations. Remote sensing methods (identification using aerial imagery) were used to identify actual locations of buildings having the characteristic size and shape of commercial poultry barns. This approach was applied to 594 U.S. counties with > 100,000 birds in 34 states based on the 2012 U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS), Census of Agriculture (CoA). The two methods were integrated in a hybrid approach to develop an automated machine learning process to locate commercial poultry operations and predict the number and type of poultry for each operation across the coterminous United States. Validation illustrated that the hybrid model had higher locational accuracy and more realistic distribution and density patterns when compared to purely simulated data. The resulting national poultry population dataset has significant potential for application in animal disease spread modelling, surveillance, emergency planning and response, economics, and other fields, providing a versatile asset for further agricultural research.
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Affiliation(s)
- Kelly A Patyk
- United States Department of Agriculture, Animal Plant and Health Inspection Service, Veterinary Services, Strategy and Policy, Center for Epidemiology and Animal Health.
| | - Mary J McCool-Eye
- United States Department of Agriculture, Animal Plant and Health Inspection Service, Veterinary Services, Strategy and Policy, Center for Epidemiology and Animal Health.
| | - David D South
- United States Department of Agriculture, Animal Plant and Health Inspection Service, Veterinary Services, Strategy and Policy, Center for Epidemiology and Animal Health.
| | - Christopher L Burdett
- Colorado State University, Department of Environmental and Radiological Health Sciences, Fort Collins, CO.
| | - Susan A Maroney
- Colorado State University, Department of Environmental and Radiological Health Sciences, Fort Collins, CO.
| | - Andrew Fox
- United States Department of Agriculture, Animal Plant and Health Inspection Service, Veterinary Services, Strategy and Policy, Center for Epidemiology and Animal Health.
| | - Grace Kuiper
- Colorado State University, Department of Environmental and Radiological Health Sciences, Fort Collins, CO.
| | - Sheryl Magzamen
- Colorado State University, Department of Environmental and Radiological Health Sciences, Fort Collins, CO.
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Lustgarten JL, Zehnder A, Shipman W, Gancher E, Webb TL. Veterinary informatics: forging the future between veterinary medicine, human medicine, and One Health initiatives-a joint paper by the Association for Veterinary Informatics (AVI) and the CTSA One Health Alliance (COHA). JAMIA Open 2020; 3:306-317. [PMID: 32734172 PMCID: PMC7382640 DOI: 10.1093/jamiaopen/ooaa005] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Revised: 12/26/2019] [Accepted: 02/26/2020] [Indexed: 12/25/2022] Open
Abstract
Objectives This manuscript reviews the current state of veterinary medical electronic health records and the ability to aggregate and analyze large datasets from multiple organizations and clinics. We also review analytical techniques as well as research efforts into veterinary informatics with a focus on applications relevant to human and animal medicine. Our goal is to provide references and context for these resources so that researchers can identify resources of interest and translational opportunities to advance the field. Methods and Results This review covers various methods of veterinary informatics including natural language processing and machine learning techniques in brief and various ongoing and future projects. After detailing techniques and sources of data, we describe some of the challenges and opportunities within veterinary informatics as well as providing reviews of common One Health techniques and specific applications that affect both humans and animals. Discussion Current limitations in the field of veterinary informatics include limited sources of training data for developing machine learning and artificial intelligence algorithms, siloed data between academic institutions, corporate institutions, and many small private practices, and inconsistent data formats that make many integration problems difficult. Despite those limitations, there have been significant advancements in the field in the last few years and continued development of a few, key, large data resources that are available for interested clinicians and researchers. These real-world use cases and applications show current and significant future potential as veterinary informatics grows in importance. Veterinary informatics can forge new possibilities within veterinary medicine and between veterinary medicine, human medicine, and One Health initiatives.
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Affiliation(s)
- Jonathan L Lustgarten
- Association for Veterinary Informatics, Dixon, California, USA.,VCA Inc., Health Technology & Informatics, Los Angeles, California, USA
| | | | - Wayde Shipman
- Veterinary Medical Databases, Columbia, Missouri, USA
| | - Elizabeth Gancher
- Department of Infectious diseases and HIV medicine, Drexel University College of Medicine, Philadelphia, Pennsylvania, USA
| | - Tracy L Webb
- Department of Clinical Sciences, Colorado State University, Fort Collins, Colorado, USA
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Sellman S, Tildesley MJ, Burdett CL, Miller RS, Hallman C, Webb CT, Wennergren U, Portacci K, Lindström T. Realistic assumptions about spatial locations and clustering of premises matter for models of foot-and-mouth disease spread in the United States. PLoS Comput Biol 2020; 16:e1007641. [PMID: 32078622 PMCID: PMC7053778 DOI: 10.1371/journal.pcbi.1007641] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 03/03/2020] [Accepted: 01/08/2020] [Indexed: 11/18/2022] Open
Abstract
Spatially explicit livestock disease models require demographic data for individual farms or premises. In the U.S., demographic data are only available aggregated at county or coarser scales, so disease models must rely on assumptions about how individual premises are distributed within counties. Here, we addressed the importance of realistic assumptions for this purpose. We compared modeling of foot and mouth disease (FMD) outbreaks using simple randomization of locations to premises configurations predicted by the Farm Location and Agricultural Production Simulator (FLAPS), which infers location based on features such as topography, land-cover, climate, and roads. We focused on three premises-level Susceptible-Exposed-Infectious-Removed models available from the literature, all using the same kernel approach but with different parameterizations and functional forms. By computing the basic reproductive number of the infection (R0) for both FLAPS and randomized configurations, we investigated how spatial locations and clustering of premises affects outbreak predictions. Further, we performed stochastic simulations to evaluate if identified differences were consistent for later stages of an outbreak. Using Ripley’s K to quantify clustering, we found that FLAPS configurations were substantially more clustered at the scales relevant for the implemented models, leading to a higher frequency of nearby premises compared to randomized configurations. As a result, R0 was typically higher in FLAPS configurations, and the simulation study corroborated the pattern for later stages of outbreaks. Further, both R0 and simulations exhibited substantial spatial heterogeneity in terms of differences between configurations. Thus, using realistic assumptions when de-aggregating locations based on available data can have a pronounced effect on epidemiological predictions, affecting if, where, and to what extent FMD may invade the population. We conclude that methods such as FLAPS should be preferred over randomization approaches. When modeling the spread of infectious livestock diseases such as foot-and-mouth disease (FMD), the distance between premises is an important aspect. In the U.S., locations of premises are not available, forcing modelers to make assumptions about their coordinates. Such assumptions can be more or less crude and will impact the conclusions drawn from the model. To investigate the impact of such assumptions, we modeled outbreaks of FMD within the cattle population of the U.S. under two assumptions about premises locations. Their position was either randomly distributed within counties or informed by a state-of-the-art method developed specifically to simulate realistic locations of agricultural operations. We found that the higher degree of spatial clustering of premises associated with more realistic assumptions about locations leads to a substantially higher risk of outbreaks. Our results also show that the amount with which the risk is under-estimated by randomizing locations is unevenly distributed across the landscape. Together, these findings show a clear support for using informed methods to determine the spatial locations of premises and highlight the importance of spatial clustering when modeling FMD-like diseases.
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Affiliation(s)
- Stefan Sellman
- Department of Physics, Chemistry and Biology, Division of Theoretical Biology, Linköping University, Linköping, Sweden
- * E-mail:
| | - Michael J. Tildesley
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, United Kingdom
| | - Christopher L. Burdett
- Department of Biology, Colorado State University, Fort Collins, Colorado, United States of America
| | - Ryan S. Miller
- Center for Epidemiology and Animal Health, United States Department of Agriculture, Fort Collins, Colorado, United States of America
| | - Clayton Hallman
- Center for Epidemiology and Animal Health, United States Department of Agriculture, Fort Collins, Colorado, United States of America
| | - Colleen T. Webb
- Department of Biology, Colorado State University, Fort Collins, Colorado, United States of America
| | - Uno Wennergren
- Department of Physics, Chemistry and Biology, Division of Theoretical Biology, Linköping University, Linköping, Sweden
| | - Katie Portacci
- Center for Epidemiology and Animal Health, United States Department of Agriculture, Fort Collins, Colorado, United States of America
| | - Tom Lindström
- Department of Physics, Chemistry and Biology, Division of Theoretical Biology, Linköping University, Linköping, Sweden
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Burdett CL, Kraus BR, Garza SJ, Miller RS, Bjork KE. Simulating the Distribution of Individual Livestock Farms and Their Populations in the United States: An Example Using Domestic Swine (Sus scrofa domesticus) Farms. PLoS One 2015; 10:e0140338. [PMID: 26571497 PMCID: PMC4646625 DOI: 10.1371/journal.pone.0140338] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2015] [Accepted: 08/28/2015] [Indexed: 11/18/2022] Open
Abstract
Livestock distribution in the United States (U.S.) can only be mapped at a county-level or worse resolution. We developed a spatial microsimulation model called the Farm Location and Agricultural Production Simulator (FLAPS) that simulated the distribution and populations of individual livestock farms throughout the conterminous U.S. Using domestic pigs (Sus scrofa domesticus) as an example species, we customized iterative proportional-fitting algorithms for the hierarchical structure of the U.S. Census of Agriculture and imputed unpublished state- or county-level livestock population totals that were redacted to ensure confidentiality. We used a weighted sampling design to collect data on the presence and absence of farms and used them to develop a national-scale distribution model that predicted the distribution of individual farms at a 100 m resolution. We implemented microsimulation algorithms that simulated the populations and locations of individual farms using output from our imputed Census of Agriculture dataset and distribution model. Approximately 19% of county-level pig population totals were unpublished in the 2012 Census of Agriculture and needed to be imputed. Using aerial photography, we confirmed the presence or absence of livestock farms at 10,238 locations and found livestock farms were correlated with open areas, cropland, and roads, and also areas with cooler temperatures and gentler topography. The distribution of swine farms was highly variable, but cross-validation of our distribution model produced an area under the receiver-operating characteristics curve value of 0.78, which indicated good predictive performance. Verification analyses showed FLAPS accurately imputed and simulated Census of Agriculture data based on absolute percent difference values of < 0.01% at the state-to-national scale, 3.26% for the county-to-state scale, and 0.03% for the individual farm-to-county scale. Our output data have many applications for risk management of agricultural systems including epidemiological studies, food safety, biosecurity issues, emergency-response planning, and conflicts between livestock and other natural resources.
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Affiliation(s)
- Christopher L. Burdett
- Colorado State University, Department of Biology, Fort Collins, Colorado, United States of America
| | - Brian R. Kraus
- Colorado State University, Department of Biology, Fort Collins, Colorado, United States of America
| | - Sarah J. Garza
- Colorado State University, Department of Biology, Fort Collins, Colorado, United States of America
| | - Ryan S. Miller
- United States Department of Agriculture, Animal and Plant Health Inspection Service, Veterinary Services, Centers for Animal Health and Epidemiology, Fort Collins, Colorado, United States of America
| | - Kathe E. Bjork
- United States Department of Agriculture, Animal and Plant Health Inspection Service, Veterinary Services, Centers for Animal Health and Epidemiology, Fort Collins, Colorado, United States of America
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