Preventive diagnosis of dairy cow lameness
This research aimed to develop a Fuzzy inference based on expert system to help preventing lameness in dairy cattle. Hoof length, nutritional parameters and floor material properties (roughness) were used to build the Fuzzy inference system. The expert system architecture was defined using Unified M...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Sociedade Brasileira de Engenharia Agrícola
2014-06-01
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| Series: | Engenharia Agrícola |
| Subjects: | |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162014000300020&tlng=en |
| _version_ | 1828133781837447168 |
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| author | Mario Mollo Neto Irenilza de A. Nääs Victor C. de Carvalho Antonio H. Q. Conceição |
| author_facet | Mario Mollo Neto Irenilza de A. Nääs Victor C. de Carvalho Antonio H. Q. Conceição |
| author_sort | Mario Mollo Neto |
| collection | DOAJ |
| description | This research aimed to develop a Fuzzy inference based on expert system to help preventing lameness in dairy cattle. Hoof length, nutritional parameters and floor material properties (roughness) were used to build the Fuzzy inference system. The expert system architecture was defined using Unified Modelling Language (UML). Data were collected in a commercial dairy herd using two different subgroups (H1 and H2), in order to validate the Fuzzy inference functions. The numbers of True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN) responses were used to build the classifier system up, after an established gold standard comparison. A Lesion Incidence Possibility (LIP) developed function indicates the chances of a cow becoming lame. The obtained lameness percentage in H1 and H2 was 8.40% and 1.77%, respectively. The system estimated a Lesion Incidence Possibility (LIP) of 5.00% and 2.00% in H1 and H2, respectively. The system simulation presented 3.40% difference from real cattle lameness data for H1, while for H2, it was 0.23%; indicating the system efficiency in decision-making. |
| first_indexed | 2024-04-11T17:24:23Z |
| format | Article |
| id | doaj.art-485ef576ce6e4756a8a6a30ed2618dea |
| institution | Directory Open Access Journal |
| issn | 0100-6916 |
| language | English |
| last_indexed | 2024-04-11T17:24:23Z |
| publishDate | 2014-06-01 |
| publisher | Sociedade Brasileira de Engenharia Agrícola |
| record_format | Article |
| series | Engenharia Agrícola |
| spelling | doaj.art-485ef576ce6e4756a8a6a30ed2618dea2022-12-22T04:12:23ZengSociedade Brasileira de Engenharia AgrícolaEngenharia Agrícola0100-69162014-06-0134357758910.1590/S0100-69162014000300020Preventive diagnosis of dairy cow lamenessMario Mollo NetoIrenilza de A. NääsVictor C. de CarvalhoAntonio H. Q. ConceiçãoThis research aimed to develop a Fuzzy inference based on expert system to help preventing lameness in dairy cattle. Hoof length, nutritional parameters and floor material properties (roughness) were used to build the Fuzzy inference system. The expert system architecture was defined using Unified Modelling Language (UML). Data were collected in a commercial dairy herd using two different subgroups (H1 and H2), in order to validate the Fuzzy inference functions. The numbers of True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN) responses were used to build the classifier system up, after an established gold standard comparison. A Lesion Incidence Possibility (LIP) developed function indicates the chances of a cow becoming lame. The obtained lameness percentage in H1 and H2 was 8.40% and 1.77%, respectively. The system estimated a Lesion Incidence Possibility (LIP) of 5.00% and 2.00% in H1 and H2, respectively. The system simulation presented 3.40% difference from real cattle lameness data for H1, while for H2, it was 0.23%; indicating the system efficiency in decision-making.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162014000300020&tlng=endecision-making supportexpert systemFuzzy inference |
| spellingShingle | Mario Mollo Neto Irenilza de A. Nääs Victor C. de Carvalho Antonio H. Q. Conceição Preventive diagnosis of dairy cow lameness Engenharia Agrícola decision-making support expert system Fuzzy inference |
| title | Preventive diagnosis of dairy cow lameness |
| title_full | Preventive diagnosis of dairy cow lameness |
| title_fullStr | Preventive diagnosis of dairy cow lameness |
| title_full_unstemmed | Preventive diagnosis of dairy cow lameness |
| title_short | Preventive diagnosis of dairy cow lameness |
| title_sort | preventive diagnosis of dairy cow lameness |
| topic | decision-making support expert system Fuzzy inference |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162014000300020&tlng=en |
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