ArticleJDS communications2025
Evaluating cow identification reliability of a camera-based locomotion and body condition scoring system in dairy cows.
Article in JDS communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Computer Vision for Cattle Health and Welfare Monitoring: A Comprehensive Review of Methods, Applications, and Interdisciplinary Integration in Smart Agriculture.Sensors (Basel, Switzerland) · 2026Review
- Assessing the reliability of camera-based identification, activity monitoring, and location in housing systems on dairy farms.JDS communications · 2026Article
- Corrigendum to "Evaluating cow identification reliability of a camera-based locomotion and body condition scoring system in dairy cows" (JDS Commun. 6:202-205).JDS communications · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dairy farms are growing in size, while the total number of farms is decreasing. As herd sizes grow, technology is increasingly adopted for monitoring and managing cows. However, for technology to replace subjective and labor-intensive tasks, accurate technologies are needed. Camera-based systems are being explored for improving management, particularly around lameness and body condition scoring. A key requirement a camera-based technology is its ability to accurately identify the animal being observed. The primary objective of this study was to evaluate the cow identification (ID) accuracy of a camera-based technology that locomotion scores and body condition scores dairy cattle. Secondary objectives were to determine the number of days required for initial identification and evaluate the frequency of animal recognition. Two dairy sites in Minnesota were visited twice and a total of 105 cows were enrolled from site A (n = 40) and site B (n = 65). Each cow had their cow ID and radio frequency identification recorded. All cows that had been in the pen for 5 or less days were enrolled in the study and had a combination of colors, letters, and numbers painted on their rumps as unique identifiers (paint ID; PID). The video feed from the camera-based technology was observed daily from the company website for 7 d. A cow was recorded as correctly identified if the website had an uploaded video for a cow's ID that matched its PID. Successful identification was defined as the proportion of cow ID for which video was uploaded to the user platform within 7 d from the start of the study, regardless of accuracy. Correct identification was subsequently calculated as the proportion of these successfully identified cows that had a PID corresponding to their PID. The days under the camera were calculated by including the time cows would have been exposed to the camera before our 7-d observation period. Additionally, days identified reflect the total number of days that cows were identified (correctly or incorrectly) and scored during the observation period. Of the 103 cows enrolled, 87 (84.5%; 95% CI: 76%-91%) of cows were successfully identified during the 7-d study period. One cow from those 87 was incorrectly identified, resulting in a correct identification of 98.9% (95% CI: 94%-100%). Of the 86 correctly identified cows, all cows were observed between days 4 and 11 under the camera. Of the cows identified, they were identified 1 to 7 times. This technology accurately identifies cows, but 16 cows were not initially identified and ended with a minimum and maximum of 7 and 11 d under the camera, respectively. To allow management decisions to be made early in lactation or for new cows entering the herd, the technology will need to accurately identify all cows within the first week of being exposed to the camera.
Identifiers
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Registered trials
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