Efficient reproduction in dairy cows is crucial for the economic viability of dairy farms. Estrus detection is a key component of dairy cow reproductive management, and traditional estrus detection methods primarily rely on human visual observation, which is time-consuming, labor-intensive, and has low accuracy and efficiency. With the development of computer technology, sensor technology, and artificial intelligence, automatic estrus detection technology for dairy cows has received increasing attention.
Early studies mainly focused on using a single sensor or detection method. Xu et al. (1998) compared a radio telemetry system (HeatWatch) and visual observation combined with tail painting for detecting estrus in grazing dairy cows, finding that the efficiency and accuracy of visual observation were 98.4% and 97.6%, respectively, while the efficiency and accuracy of the HeatWatch system were 91.7% and 100%, respectively. Rae et al. (1999) evaluated the effect of visual observation and a pressure-sensitive detection device on estrus detection in beef cattle, finding that the pregnancy rate within 25 days was 60.5% in the identified cows in the visual observation group, which was higher than the 45.8% in the pressure-sensitive detection device group. Roelofs et al. (2005) explored the feasibility of using pedometer readings as an indicator for estrus detection and ovulation time prediction in dairy cows, and achieved estrus detection efficiencies ranging from 51% to 87%. Peralta et al. (2005) compared the performance of the HeatWatch device, an ALPRO activity sensor, and visual observation three times daily for estrus detection in hot summer conditions, with the highest estrus detection efficiency of 80.2% achieved when the three systems were used in combination. Palmer et al. (2010) found that, compared to indoor housing, the efficiency of all three detection methods (visual observation, tail painting, and HeatWatch) was higher under grazing conditions, but there was no difference in accuracy. Løvendahl and Chagunda (2010) constructed an algorithm for detecting and describing behavioral estrus in dairy cows using hourly recorded activity data and exponential smoothing deviations.
As research progressed, multi-sensor data fusion and machine learning methods began to be applied to estrus detection in dairy cows. Mayo et al. (2019) evaluated the effect of using multiple commercial precision dairy cow monitoring technologies in combination for estrus detection, finding that they could achieve at least the same detection effect as visual observation, with four technologies having a detection efficiency 15% to 35% higher than visual observation. Fricke et al. (2014) analyzed data from 2,661 artificial inseminations and determined that the optimal insemination time for dairy cows using a radio telemetry system for estrus detection was within 4–12 h after the first standing activity. Aungier et al. (2012) used a neck-mounted activity monitor to explore the influence of cow-related factors on the relationship between activity and estrous behavior, and improved the accuracy of estrus detection to 87.5% by adjusting the activity duration threshold. Chanvallon et al. (2014) compared the performance of pedometers and two activity monitors, finding that the sensitivity of pedometers was higher than that of the activity monitors, but the latter had a higher positive predictive value. Rutten et al. (2014) demonstrated through modeling analysis that investing in activity monitors for automatic estrus detection was economically feasible.
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