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Shine P, Murphy MD. Over 20 Years of Machine Learning Applications on Dairy Farms: A Comprehensive Mapping Study. SENSORS (BASEL, SWITZERLAND) 2021; 22:52. [PMID: 35009593 PMCID: PMC8747441 DOI: 10.3390/s22010052] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 12/17/2021] [Accepted: 12/19/2021] [Indexed: 05/06/2023]
Abstract
Machine learning applications are becoming more ubiquitous in dairy farming decision support applications in areas such as feeding, animal husbandry, healthcare, animal behavior, milking and resource management. Thus, the objective of this mapping study was to collate and assess studies published in journals and conference proceedings between 1999 and 2021, which applied machine learning algorithms to dairy farming-related problems to identify trends in the geographical origins of data, as well as the algorithms, features and evaluation metrics and methods used. This mapping study was carried out in line with PRISMA guidelines, with six pre-defined research questions (RQ) and a broad and unbiased search strategy that explored five databases. In total, 129 publications passed the pre-defined selection criteria, from which relevant data required to answer each RQ were extracted and analyzed. This study found that Europe (43% of studies) produced the largest number of publications (RQ1), while the largest number of articles were published in the Computers and Electronics in Agriculture journal (21%) (RQ2). The largest number of studies addressed problems related to the physiology and health of dairy cows (32%) (RQ3), while the most frequently employed feature data were derived from sensors (48%) (RQ4). The largest number of studies employed tree-based algorithms (54%) (RQ5), while RMSE (56%) (regression) and accuracy (77%) (classification) were the most frequently employed metrics used, and hold-out cross-validation (39%) was the most frequently employed evaluation method (RQ6). Since 2018, there has been more than a sevenfold increase in the number of studies that focused on the physiology and health of dairy cows, compared to almost a threefold increase in the overall number of publications, suggesting an increased focus on this subdomain. In addition, a fivefold increase in the number of publications that employed neural network algorithms was identified since 2018, in comparison to a threefold increase in the use of both tree-based algorithms and statistical regression algorithms, suggesting an increasing utilization of neural network-based algorithms.
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Affiliation(s)
| | - Michael D. Murphy
- Department of Process, Energy and Transport Engineering, Munster Technological University, T12 P928 Cork, Ireland;
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Schwarz D, Rosenberg Bak M, Waaben Hansen P. Development of global fatty acid models and possible applications. INT J DAIRY TECHNOL 2021. [DOI: 10.1111/1471-0307.12820] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Daniel Schwarz
- FOSS Analytical A/S Nils Foss Alle 1 Hilleroed 3400Denmark
| | | | - Per Waaben Hansen
- FOSS Analytical A/S Nils Foss Alle 1 Hilleroed 3400Denmark
- Department of Food Science Faculty of Science Copenhagen University Rolighedsvej 26 Frederiksberg 1958 Denmark
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Tedde A, Grelet C, Ho PN, Pryce JE, Hailemariam D, Wang Z, Plastow G, Gengler N, Brostaux Y, Froidmont E, Dehareng F, Bertozzi C, Crowe MA, Dufrasne I, Soyeurt H. Validation of Dairy Cow Bodyweight Prediction Using Traits Easily Recorded by Dairy Herd Improvement Organizations and Its Potential Improvement Using Feature Selection Algorithms. Animals (Basel) 2021; 11:1288. [PMID: 33946238 PMCID: PMC8145206 DOI: 10.3390/ani11051288] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/27/2021] [Accepted: 04/28/2021] [Indexed: 01/22/2023] Open
Abstract
Knowing the body weight (BW) of a cow at a specific moment or measuring its changes through time is of interest for management purposes. The current work aimed to validate the feasibility of predicting BW using the day in milk, parity, milk yield, and milk mid-infrared (MIR) spectrum from a multiple-country dataset and reduce the number of predictors to limit the risk of over-fitting and potentially improve its accuracy. The BW modeling procedure involved feature selections and herd-independent validation in identifying the most interesting subsets of predictors and then external validation of the models. From 1849 records collected in 9 herds from 360 Holstein cows, the best performing models achieved a root mean square error (RMSE) for the herd-independent validation between 52 ± 2.34 kg to 56 ± 3.16 kg, including from 5 to 62 predictors. Among these models, three performed remarkably well in external validation using an independent dataset (N = 4067), resulting in RMSE ranging from 52 to 56 kg. The results suggest that multiple optimal BW predictive models coexist due to the high correlations between adjacent spectral points.
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Affiliation(s)
- Anthony Tedde
- AGROBIOCHEM Department, Research and Teaching Centre (TERRA), Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium; (N.G.); (Y.B.); (H.S.)
- National Funds for Scientific Research, 1000 Brussels, Belgium
| | - Clément Grelet
- Walloon Agricultural Research Center (CRA-W), 5030 Gembloux, Belgium; (C.G.); (E.F.); (F.D.)
| | - Phuong N. Ho
- Agriculture Victoria Research, Centre for AgriBioscience, AgriBio, Bundoora, VIC 3083, Australia; (P.N.H.); (J.E.P.)
| | - Jennie E. Pryce
- Agriculture Victoria Research, Centre for AgriBioscience, AgriBio, Bundoora, VIC 3083, Australia; (P.N.H.); (J.E.P.)
- School of Applied Systems Biology, La Trobe University, 5 Ring Road, Bundoora, VIC 3083, Australia
| | - Dagnachew Hailemariam
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada; (D.H.); (Z.W.); (G.P.)
| | - Zhiquan Wang
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada; (D.H.); (Z.W.); (G.P.)
| | - Graham Plastow
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada; (D.H.); (Z.W.); (G.P.)
| | - Nicolas Gengler
- AGROBIOCHEM Department, Research and Teaching Centre (TERRA), Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium; (N.G.); (Y.B.); (H.S.)
| | - Yves Brostaux
- AGROBIOCHEM Department, Research and Teaching Centre (TERRA), Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium; (N.G.); (Y.B.); (H.S.)
| | - Eric Froidmont
- Walloon Agricultural Research Center (CRA-W), 5030 Gembloux, Belgium; (C.G.); (E.F.); (F.D.)
| | - Frédéric Dehareng
- Walloon Agricultural Research Center (CRA-W), 5030 Gembloux, Belgium; (C.G.); (E.F.); (F.D.)
| | | | - Mark A. Crowe
- UCD School of Veterinary Medicine, University College Dublin, D04 V1W8 Dublin, Ireland;
| | - Isabelle Dufrasne
- Faculty of Veterinary Medicine, University of Liège, Quartier Vallée 2, 4000 Liège, Belgium;
| | | | - Hélène Soyeurt
- AGROBIOCHEM Department, Research and Teaching Centre (TERRA), Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium; (N.G.); (Y.B.); (H.S.)
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