A Two-Stage Machine Learning Approach for Calving Detection in Rangeland Cattle
Monitoring parturient cattle during calving is crucial for reducing cow and calf mortality, enhancing reproductive and production performance, and minimizing labor costs. Traditional monitoring methods include direct animal inspection or the use of specialized sensors. These methods can be effective...
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MDPI AG
2025-07-01
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| author | Yuxi Wang Andrés Perea Huiping Cao Mehmet Bakir Santiago Utsumi |
| author_facet | Yuxi Wang Andrés Perea Huiping Cao Mehmet Bakir Santiago Utsumi |
| author_sort | Yuxi Wang |
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| description | Monitoring parturient cattle during calving is crucial for reducing cow and calf mortality, enhancing reproductive and production performance, and minimizing labor costs. Traditional monitoring methods include direct animal inspection or the use of specialized sensors. These methods can be effective, but impractical in large-scale ranching operations due to time, cost, and logistical constraints. To address this challenge, a network of low-power and long-range IoT sensors combining the Global Navigation Satellite System (GNSS) and tri-axial accelerometers was deployed to monitor in real-time 15 parturient Brangus cows on a 700-hectare pasture at the Chihuahuan Desert Rangeland Research Center (CDRRC). A two-stage machine learning approach was tested. In the first stage, a fully connected autoencoder with time encoding was used for unsupervised detection of anomalous behavior. In the second stage, a Random Forest classifier was applied to distinguish calving events from other detected anomalies. A 5-fold cross-validation, using 12 cows for training and 3 cows for testing, was applied at each iteration. While 100% of the calving events were successfully detected by the autoencoder, the Random Forest model failed to classify the calving events of two cows and misidentified the onset of calving for a third cow by 46 h. The proposed framework demonstrates the value of combining unsupervised and supervised machine learning techniques for detecting calving events in rangeland cattle under extensive management conditions. The real-time application of the proposed AI-driven monitoring system has the potential to enhance animal welfare and productivity, improve operational efficiency, and reduce labor demands in large-scale ranching. Future advancements in multi-sensor platforms and model refinements could further boost detection accuracy, making this approach increasingly adaptable across diverse management systems, herd structures, and environmental conditions. |
| format | Article |
| id | doaj-art-40ff22df8fa849b7b4c6724b61d0a3be |
| institution | Matheson Library |
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| publishDate | 2025-07-01 |
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| series | Agriculture |
| spelling | doaj-art-40ff22df8fa849b7b4c6724b61d0a3be2025-07-11T14:34:42ZengMDPI AGAgriculture2077-04722025-07-011513143410.3390/agriculture15131434A Two-Stage Machine Learning Approach for Calving Detection in Rangeland CattleYuxi Wang0Andrés Perea1Huiping Cao2Mehmet Bakir3Santiago Utsumi4Department of Computer Science, New Mexico State University, Las Cruces, NM 88003, USADepartment of Animal and Range Sciences, New Mexico State University, Las Cruces, NM 88003, USADepartment of Computer Science, New Mexico State University, Las Cruces, NM 88003, USADepartment of Animal and Range Sciences, New Mexico State University, Las Cruces, NM 88003, USADepartment of Animal and Range Sciences, New Mexico State University, Las Cruces, NM 88003, USAMonitoring parturient cattle during calving is crucial for reducing cow and calf mortality, enhancing reproductive and production performance, and minimizing labor costs. Traditional monitoring methods include direct animal inspection or the use of specialized sensors. These methods can be effective, but impractical in large-scale ranching operations due to time, cost, and logistical constraints. To address this challenge, a network of low-power and long-range IoT sensors combining the Global Navigation Satellite System (GNSS) and tri-axial accelerometers was deployed to monitor in real-time 15 parturient Brangus cows on a 700-hectare pasture at the Chihuahuan Desert Rangeland Research Center (CDRRC). A two-stage machine learning approach was tested. In the first stage, a fully connected autoencoder with time encoding was used for unsupervised detection of anomalous behavior. In the second stage, a Random Forest classifier was applied to distinguish calving events from other detected anomalies. A 5-fold cross-validation, using 12 cows for training and 3 cows for testing, was applied at each iteration. While 100% of the calving events were successfully detected by the autoencoder, the Random Forest model failed to classify the calving events of two cows and misidentified the onset of calving for a third cow by 46 h. The proposed framework demonstrates the value of combining unsupervised and supervised machine learning techniques for detecting calving events in rangeland cattle under extensive management conditions. The real-time application of the proposed AI-driven monitoring system has the potential to enhance animal welfare and productivity, improve operational efficiency, and reduce labor demands in large-scale ranching. Future advancements in multi-sensor platforms and model refinements could further boost detection accuracy, making this approach increasingly adaptable across diverse management systems, herd structures, and environmental conditions.https://www.mdpi.com/2077-0472/15/13/1434calving detectionanomaly detectionunsupervised modelsupervised modelGNSSaccelerometer |
| spellingShingle | Yuxi Wang Andrés Perea Huiping Cao Mehmet Bakir Santiago Utsumi A Two-Stage Machine Learning Approach for Calving Detection in Rangeland Cattle Agriculture calving detection anomaly detection unsupervised model supervised model GNSS accelerometer |
| title | A Two-Stage Machine Learning Approach for Calving Detection in Rangeland Cattle |
| title_full | A Two-Stage Machine Learning Approach for Calving Detection in Rangeland Cattle |
| title_fullStr | A Two-Stage Machine Learning Approach for Calving Detection in Rangeland Cattle |
| title_full_unstemmed | A Two-Stage Machine Learning Approach for Calving Detection in Rangeland Cattle |
| title_short | A Two-Stage Machine Learning Approach for Calving Detection in Rangeland Cattle |
| title_sort | two stage machine learning approach for calving detection in rangeland cattle |
| topic | calving detection anomaly detection unsupervised model supervised model GNSS accelerometer |
| url | https://www.mdpi.com/2077-0472/15/13/1434 |
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