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4 results for “cow behavior”
Japanese Black Beef Cow Behavior Classification Dataset
<p>Licensed under:<br> Attribution-NonCommercial-NoDerivatives 4.0 International<br> <a href="https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode">https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode</a></p> <p><strong>Japanese Black Beef Cow Behavior Classification Dataset</strong></p> <p>This dataset contains tri-axial accelerometer sensor data with thirteen different labeled cow behaviors. This data was gathered with a 16bit +/- 2g Kionix KX122-1037 accelerometer attached to the neck of six different Japanese Black Beef Cows (`cow1.csv`-`cow6.csv`) at a cow farm of Shinshu University in Nagano, Japan on the 12th of June, 2020.</p> <p>The data gathering took place over the course of one day in which the cows were allowed to roam freely in two different areas, namely, a grass field and farm pens, while being filmed with Sony FDR-X3000 4K video cameras.</p> <p>The timestamps of the video and accelerometer data were matched while human observers which included behavior experts and non-experts labeled the data from the video footage. The labeling and data gathering took a total of 69 person-hours.</p> <p>567 minutes of unlabeled data were parsed into 197 minutes of high-quality labeled data comprising thirteen behaviors by means of majority voting with three annotators. The time per behavior in number of samples (@25Hz) and their respective descriptions are shown in the following table:</p> <table> <thead> <tr> <th scope="col"> </th> <th scope="col">Cow 1</th> <th scope="col">Cow 2</th> <th scope="col">Cow 3</th> <th scope="col">Cow 4</th> <th scope="col">Cow 5</th> <th scope="col">Cow 6</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>RES</td> <td>35814</td> <td>47059</td> <td>20501</td> <td>15735</td> <td>11025</td> <td>19996</td> <td>Resting in standing position</td> </tr> <tr> <td>RUS</td> <td>1620</td> <td>25930</td> <td>11156</td> <td>14523</td> <td>0</td> <td>0</td> <td>Ruminating in standing position</td> </tr> <tr> <td>MOV</td> <td>6376</td> <td>8437</td> <td>7532</td> <td>17248</td> <td>4846</td> <td>5760</td> <td>Moving</td> </tr> <tr> <td>GRZ</td> <td>2416</td> <td>2199</td> <td>0</td> <td>2707</td> <td>2442</td> <td>7849</td> <td>Grazing</td> </tr> <tr> <td>SLT</td> <td>204</td> <td>0</td> <td>10654</td> <td>0</td> <td>0</td> <td>0</td> <td>Salt licking</td> </tr> <tr> <td>FES</td> <td>6809</td> <td>0</td> <td>0</td> <td>0</td> <td>1125</td> <td>0</td> <td>Feeding in stanchion</td> </tr> <tr> <td>DRN</td> <td>1176</td> <td>0</td> <td>1300</td> <td>0</td> <td>0</td> <td>0</td> <td>Drinking</td> </tr> <tr> <td>LCK</td> <td>0</td> <td>0</td> <td>649</td> <td>297</td> <td>0</td> <td>356</td> <td>Licking</td> </tr> <tr> <td>REL</td> <td>0 </td> <td>360</td> <td>0</td> <td>404</td> <td>0</td> <td>0</td> <td>Resting in lying position</td> </tr> <tr> <td>URI</td> <td>239</td> <td>0</td> <td>383</td> <td>0</td> <td>0</td> <td>0</td> <td>Urinating</td> </tr> <tr> <td>ATT</td> <td>57</td> <td>50</td> <td>0</td> <td>62</td> <td>0</td> <td>197</td> <td>Attacking</td> </tr> <tr> <td>ESC</td> <td>0</td> <td>0</td> <td>0</td> <td>128</td> <td>0</td> <td>0</td> <td>Escaping</td> </tr> <tr> <td>BMN</td> <td>0</td> <td>54</td> <td>0</td> <td>0</td> <td>0</td> <td>0</td> <td>Being mounted</td> </tr> <tr> <td>ETC</td> <td>105917</td> <td>103084</td> <td>129297</td> <td>62064</td> <td>53922</td> <td>100571</td> <td>Other behaviors</td> </tr> <tr> <td>BLN</td> <td>151249</td> <td>82599</td> <td>88431</td> <td>111744</td> <td>61544</td> <td>45128</td> <td>Data without video, no label</td> </tr> <tr> <td>Sum</td> <td>311876</td> <td>269772</td> <td>269903</td> <td>224912</td> <td>134904</td> <td>179857</td> <td> </td> </tr> </tbody> </table> <p>Accelerometer sampling rate was set to 25Hz.</p> <p>The data is split into six .csv files which represents each of the 6 cows above. The columns of these files are defined as follows:</p> <table> <thead> <tr> <th scope="col">TimeStamp_UNIX [-]</th> <th scope="col">TimeStamp_JST [-]</th> <th scope="col">AccX [g]</th> <th scope="col">AccY [g]</th> <th scope="col">AccZ [g]</th> <th scope="col">Label [-]</th> </tr> </thead> <tbody> <tr> <td>GPS timestamp in UNIX</td> <td>GPS timestamp in JST</td> <td>X-axis acceleration</td> <td>Y-axis acceleration</td> <td>z-axis acceleration</td> <td>labeled behavior</td> </tr> </tbody> </table> <p>The gathering of this data with these cows was reviewed and approved by the Institutional Animal Care and Use Committee of Shinshu University.</p> <p><strong>Version History</strong></p> <p>v1.0.0: Release on 24th of September, 2021. First version.</p> <p>v2.0.0: This version. UNIX and Japan Standard Time (JST) time stamps are added for each .csv file of cow1-6. Added explanations of behaviors for ETC and BLN. More information on publications that use this dataset, data logger software that has been developed for this project.</p> <p><strong>Data logger open source software</strong></p> <p>Software developed for the data logger that was used to gather this dataset, Sony's IoT development board SPRESENSE, CXD5602PWBMAIN1. The function of this data logger is to write inertia sensor data along with timestamps. Timestamp data is corrected with GPS signal. Available in Arduino development environment.</p> <p><a href="https://zenodo.org/record/5848608#.YeFF9NHP3Z8">https://zenodo.org/record/5848608#.YeFF9NHP3Z8</a></p> <p><strong>Publications using this dataset</strong></p> <p><a href="https://ieeexplore.ieee.org/abstract/document/9566833">[1] Li, Chao, et al. "Data Augmentation for Inertial Sensor Data in CNNs for Cattle Behavior Classification." IEEE Sensors Letters 5.11 (2021): 1-4.</a></p> <p><a href="https://ieeexplore.ieee.org/abstract/document/9401342">[2] Bartels, Jim, et al. "A 216 microW, 87% Accurate Cow Behavior Classifying Decision Tree on FPGA With Interpolated Arctan2." 2021 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2021.</a></p>
Environmental and behavioral sensor data to identify heat stress in dairy cows
Heat stress impairs the health and performance of dairy cows, yet only a few studies have investigated the diversity of cattle behavioral responses to heat waves. This research was conducted on an Italian Holstein dairy farm equipped with precision livestock farming sensors to assess potential different behavioral patterns of the animals. Three heat waves, defined as at least five consecutive days with mean daily temperature-humidity index higher than 72, were recorded in the farm area during the summer of 2021. Individual daily milk yield data of 102 cows were used to identify ‘heat-sensitive’ animals, meaning the cows that, under a given heat wave, experienced a milk yield drop that was not linked with other health events (e.g., mastitis). Milk yield drops were detected as perturbations of the lactation curve estimated by iteratively using Wood’s equation. Individual daily minutes of lying, chewing, and activity were retrieved from ear-tag-based accelerometer sensors. Semi-parametric generalized estimating equations models were used to assess behavioral deviations of heat-sensitive cows from the herd means under heat stress conditions. Heat waves were associated with an overall increase in the herd’s chewing and activity times, along with an overall decrease of lying time. Heat-sensitive cows spent approximately 15 min/d more chewing and performing activities (p < 0.05). The findings of this research suggest that the information provided by high-frequency sensor data could assist farmers in identifying cows for which personalized interventions to alleviate heat stress are needed.
Dataset for "On Developing an ML-Based Approach for the Automatic Characterization of Behavioral Phenotypes for Dairy Cows Relevant to Thermotolerance"
<p>This dataset consists of 3,421 videos filmed at T & K Dairy in Snyder, TX, over a 24-hour period on March 12-13, 2023. These videos were then used to train, validate, and evaluate a computer vision algorithm that is capable of automatically identifying cows using their coat patterns.</p>
The Effect of Early Childhood Cow's Milk Allergy Elimination Diet on Eating Behaviors, Nutrition, and Growth Status
ClinicalTrials.gov study NCT04576845. IPD Sharing: NO. Countries: 1. Publications: 4.
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