Name
Title | ||
|---|---|---|
Evaluation Of Beef Cattle Temperament By Eye Temperature Using Infrared Thermography Technology |
Abstract | ||
|---|---|---|
Beef cattle temperament can impact animal performance and meat quality in cattle. The aim of this study was to evaluate the use of infrared thermography (IRT) maximum, average, and minimum eye temperature (IRTMIN, IRTAVG, IRTMAX) and percentage of the eye with the sclera visible in a digital image (eye white percentage, EW) to predict beef cattle temperament by (1) assessing the relationship between IRT and EW traits with 4 established subjective and objective temperament scoring methods (n = 16 traits total) and (2) identify behavioral characteristics that IRT and EW traits are predicting. Traits were measured on Angus- and Hereford-influenced steers (n = 203; age 183.00 +/- 15.80 days; BW 264.50 +/- 30.84 kg) and heifers (n = 200; age 186.60 +/- 15.18 days; BW 251.90 +/- 28.77 kg). Computer vision and image processing technologies were used to extract IRTMIN, IRTAVG, IRTMAX, and EW features. Temperament Scores (TS), Docility Scores (DS), Four-platform Standing Scale (FPSS), Qualitative Behavioral Assessment (QBA) attributes were measured to compare with the imagery methods. Results showed there were no statistically significant relationships of IRTMIN, IRTAVG IRTMAX for TS, DS, FPSS and QBA. The correlations value between EW and DS and between EW and TS were also low but indicated correlated at certain level (0.258 and 0.179 at P < 0.0001). This study showed to prove using thermal and eye white area to objectively predict beef cattle temperament, more cattle sample numbers should be further investigated to validate the hypothesis. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.compag.2021.106321 | COMPUTERS AND ELECTRONICS IN AGRICULTURE |
Keywords | DocType | Volume |
Temperament, Beef Cattle, Thermography Technology | Journal | 188 |
ISSN | Citations | PageRank |
0168-1699 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoming Chen | 1 | 0 | 0.34 |
| William Ogdahl | 2 | 0 | 0.34 |
| Lauren L. Hulsman Hanna | 3 | 0 | 0.34 |
| Carl R. Dahlen | 4 | 0 | 0.34 |
| David G. Riley | 5 | 0 | 0.34 |
| Sarah A. Wagner | 6 | 0 | 0.34 |
| Eric P. Berg | 7 | 0 | 0.34 |
| Xin Sun | 8 | 0 | 0.34 |