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Field Validation of a Visible-Light Camera-Based Calving Detection System in Commercial Beef Cattle Farms.
Hisashi Nabenishi1, Miku Odaka1, Miyu Mochizuki1
1Laboratory of Animal Feeding and Management, Department of Animal Science, School of Veterinary Medicine, Kitasato University, Aomori, Japan.
Animal Science Journal = Nihon Chikusan Gakkaiho
|July 6, 2026
Summary
A new camera system using artificial intelligence effectively detects cow calving events. This calving detection system achieved 94.1% sensitivity, offering a practical tool for beef cattle management.
Area of Science:
- Animal Science
- Agricultural Technology
Background:
- Periparturient cow monitoring is crucial for reducing calving-related losses and labor.
- Current methods may be labor-intensive or invasive.
Purpose of the Study:
- To evaluate a visible-light camera-based system for automated calving detection in beef cattle.
- To assess the system's sensitivity and predictive value under commercial farm conditions.
Main Methods:
- Deployed 61 cameras across 31 beef cattle farms to monitor 357 calving events.
- Utilized an artificial intelligence system to analyze behavioral parameters (movement, posture, tail-raising).
- Generated alerts when calving probability exceeded 70%.
Main Results:
- The system detected 336 of 357 calving events (94.1% sensitivity).
- Achieved a positive predictive value of 70.1% with 448 successful and 191 early alerts.
- Mean alert-to-calving interval was 192 minutes; alert frequency increased as calving neared.
Conclusions:
- Visible camera-based monitoring is an effective, non-invasive tool for calving detection.
- The system shows potential for practical application in improving beef cattle calving management.
- AI-driven behavioral analysis enhances the efficiency of monitoring periparturient cows.
