Portable Radar for Noncontact Heart Rate Monitoring and Estrus Detection in Dairy Cows

Potential of an activity index combining acceleration and location for automated estrus detection in dairy cows

A critical aspect of dairy management involves maintaining accurate records of individual animals and promptly detecting estrus behavior, which is vital for successful breeding and reproductive outcomes. Recent advancements in precision livestock management, utilizing computer vision and machine learning technologies, have paved the way for automated and precise solutions in these areas. The objective of this study is to investigate the application of computer vision and machine learning for animal identification and behavioral estrus detection in dairy cows housed in freestall settings, employing a two-layered algorithmic approach. The research was conducted at the Joe Bearden Dairy Research Center, where a total of 10 lactating Holstein-Friesian cows were assessed across a 7-d trial period. For synchronization efforts, cows were provided a single injection of Prostaglandin F2α at trial initiation with expected estrus behaviors to be exhibited within the following 56 h. Continuous video was captured in 4k resolution using a five-camera closed caption media recording system strategically placed in the pen corners and over the feed bunk alleyway to capture multiple perspectives. Video data extrapolated into frames and manual annotation was utilized to generate the machine learning algorithm’s training data. Frames were randomly selected from a scattering of 56 h of video across all five camera viewpoints and down sampled to 384 x 640 p for both layers. A two-layer approach was employed utilizing an algorithm for each aspect of the study with the first model detecting the behavior, and the second model using the detected region of interest to identify specific cows. The first layer was trained on 682 frames and validated against 123 manually annotated frames. The second layer was trained on 102 frames and validated against 16 frames manually annotated for individual animals. Results demonstrated that the model used for mounting behavior identification has high precision (0.819) and recall (0.89), achieving a 0.926 mAP at an IoU of 0.5. This model achieved an F1 score of 0.86 at a confidence threshold of 0.268. The results demonstrated that the model used for individual animal identification across different classes achieved high precision (0.861) and recall (0.842), with a mAP of 0.897 at an IoU of 0.5. The F1 score for all classes was 0.89 at a confidence threshold of 0.788. In conclusion, these promising findings underscore the potential of computer vision and machine learning technologies to revolutionize dairy cow management practices. This innovative approach not only has potential to enhance operational efficiency, but also contributes significantly to improved reproductive management, thereby advancing precision and productivity in dairy operations.

Insufficient herd fertility due to poor estrus detection significantly affects the dairy cattle sector. In India, the conventional way of detection relies heavily on herder’s visual surveillance, which makes the quality of estrus detection highly subjective to herder’s experience in identifying estrus signs. This method lacks automation and may not be effective in monitoring larger herds. Current devices used for cattle monitoring are imported, limited in services, and are often expensive. Continuous monitoring also suffers from poor battery life and is a deterrent for wide adoption. A low-cost system with long battery life and real-time monitoring could provide tremendous benefits to the Indian dairy industry. In this context, this paper presents our efforts to develop an IoT-based estrus monitoring and detection system that utilizes ICT technique to provide an effective map of the cattle reproduction phase and identify underlying health issues to determine the wellness of the animal specifically for Indian conditions. The system is developed to adapt to various deployment challenges such as frequent topological changes due to cattle’s mobility, limited power source, and short transmission range of sensors. In this context, the performance of the proposed system has been evaluated through laboratory trials.KeywordsEstrus detectionInternet-of-thingsDairyActivity monitoringPedometers

Oestrus detection in dairy cows has been the subject of many studies and various methods of improving oestrus detection have been investigated and reviewed by Boyd ( 1984). Poor oestrus detection will extend the calving to conception interval by prolonging both the calving to first service and first service to conception intervals (Eddy 1980). The availability of a commercially produced enzyme immunoassay (EIA) test kit (Ovucheck; Cambridge Veterinary Sciences) stimulated the setting up of trials to study oestrus detection rates and detection efficiency after first service. This paper reports the results of these trials. Trial 1 demonstrated that the E.I.A. for progesterone could be readily performed in the practice laboratory and Trial 2 demonstrated that similar improvements in accuracy and detection rates could be achieved by alternate day sampling. Oestrus detection remains a problem in many large dairy herds using artificial insemination and these trials demonstrate a place for oestrus prediction using E.I.A.

Automation of oestrus detection in dairy cows: a review

AIM To evaluate the performance of a novel accelerometer-based oestrus detection system (ODS) for dairy cows on pasture, in comparison with measurement of concentrations of progesterone in milk, ultrasonographic examination of ovaries and farmer observations. METHODS Mixed-breed lactating dairy cows (n=109) in a commercial, seasonal-calving herd managed at pasture under typical farming conditions in Ireland, were fitted with oestrus detection collars 3 weeks prior to mating start date. The ODS performed multimetric analysis of eight different motion patterns to generate oestrus alerts. Data were collected during the artificial insemination period of 66 days, commencing on 16 April 2015. Transrectal ultrasonographic examinations of the reproductive tract and measurements of concentrations of progesterone in milk were used to confirm oestrus events. Visual observations by the farmer and the number of theoretically expected oestrus events were used to evaluate the number of false negative ODS alerts. The percentage of eligible cows that were detected in oestrus at least once (and were confirmed true positives) was calculated for the first 21, 42 and 63 days of the insemination period. RESULTS During the insemination period, the ODS generated 194 oestrus alerts and 140 (72.2%) were confirmed as true positives. Six confirmed oestrus events recognised by the farmer did not generate ODS alerts. The positive predictive value of the ODS was 72.2 (95% CI=65.3–78.4)%. To account for oestrus events not identified by the ODS or the farmer, four theoretical missed oestrus events were added to the false negatives. Estimated sensitivity of the automated ODS was 93.3 (95% CI=88.1–96.8)%. The proportion of eligible cows that were detected in oestrus during the first 21 days of the insemination period was 92/106 (86.8%), and during the first 42 and 63 days of the insemination period was 103/106 (97.2%) and 105/106 (99.1%), respectively. CONCLUSIONS and CLINICAL RELEVANCE The ODS under investigation was suitable for oestrus detection in dairy cows on pasture and showed a high sensitivity of oestrus detection. Multimetric analysis of behavioural data seems to be the superior approach to developing and improving ODS for dairy cows on pasture. Due to a high proportion of false positive alerts, its use as a stand-alone system for oestrus detection cannot be recommended. As it is the first time the system was investigated, testing on other farms would be necessary for further validation.

Discrimination strategy using machine learning technique for oestrus detection in dairy cows by a dual-channel-based acoustic tag

Oestrus detection in dairy cows by using atrous spatial pyramid and attention mechanism

This study was conducted to evaluate the association between ovarian structures, uterine characteristics and physical attributes of cervico-vaginal mucus (CVM) with conception rate in normal cyclic dairy cows (N=20) at estrus. Different physical parameters of CVM at estrus such as quantity, consistency, color, pH, Spinnbarkeit value and fern pattern were evaluated. Transrectal ultrasonography was done at estrus to carry out the morphometry of follicles and uterine horns as well as following artificial insemination, the mid-cyclic corpus luteum size and functionality were assessed on the 10th day using B-mode and color Doppler ultrasonography. Among the physical parameters, significantly higher (p<0.05) Spinnbarkeit values were recorded for subsequently pregnant (n=12) as compared to non-pregnant cows (n=8). The mean diameter of the preovulatory follicle at estrus and corpus luteum vascularity (%) was significantly higher (p<0.05) in subsequently pregnant as compared to non-pregnant cows while, no significant difference (p>0.05) was recorded for intraluminal uterine fluid diameter and endometrium thickness. In conclusion, higher spinnbarkeit and preovulatory follicle diameter at estrus led to a better conception rate in dairy cows.

BACKGROUND: Early resumption of ovarian activity after parturition could positively influence the fertility of dairy cows. Ovulation of the first postpartum follicular wave and formation of corpus luteum that secretes progesterone can sub-stantially benefit the resumption of ovarian cyclicity in cows. In this regard, the presence of a dominant follicle in the ovary before day 10 postpartum could enhance ovulation rate in the first postpartum follicular wave. Pregnant mare serum gon-adotropin (PMSG) is one of the hormones with the potential to improve the growth of follicles. OBJECTIVES: Accordingly, the present study was conducted to evaluate the effect of PMSG injection six days after parturition on reproductive parameters in dairy cows. METHODS: The subjects were randomly assigned to the two groups of control (N=74) and treatment (N=79). Cows in the control group received no treatment, while animals in the treatment group received 500 IU PMSG on day six postpartum. In both experimental groups, cows were subjected to Presynch-Ovsynch protocol 30-35 days postpartum. The subjects that experienced estrus during the Presynch-Ovsynch protocol were inseminated. On the other hand, the cows that were not in estrus during this period were subjected to fixed-time insemination at the end of the Presynch-Ovsynch protocol. Pregnancy was diagnosed 45 days after artificial insemination using the rectal examination. RESULTS: In the control group, 66.22% of cows were inseminated based on estrus detection and 33.78% of cows were inseminated at fixed times. In the intervention group, 68.35% of cows were inseminated based on estrus detection and 31.65% of cows were inseminated at fixed times. The first-service conception rate (FSCR) was 27.03% and 48.1% in the control and treatment groups, respectively (p =0.01). CONCLUSIONS: The present study showed that the injection of 500 IU PMSG six days after parturition could enhance FSCR in dairy cows.

Oestrus Detection in Dairy Cows Using Likelihood Ratio Tests

Identification of oestrus cows based on vocalisation characteristics and machine learning technique using a dual-channel-equipped acoustic tag

Frequency modulated continuous wave radar-based system for monitoring dairy cow respiration rate

BackgroundRemote telemonitoring holds great potential to augment management of patients with coronary heart disease (CHD) and atrial fibrillation (AF) by enabling regular physiological monitoring during physical activity. Remote physiological monitoring may improve home and community exercise-based cardiac rehabilitation (exCR) programs and could improve assessment of the impact and management of pharmacological interventions for heart rate control in individuals with AF.ObjectiveOur aim was to evaluate the measurement validity and data transmission reliability of a remote telemonitoring system comprising a wireless multi-parameter physiological sensor, custom mobile app, and middleware platform, among individuals in sinus rhythm and AF.MethodsParticipants in sinus rhythm and with AF undertook simulated daily activities, low, moderate, and/or high intensity exercise. Remote monitoring system heart rate and respiratory rate were compared to reference measures (12-lead ECG and indirect calorimeter). Wireless data transmission loss was calculated between the sensor, mobile app, and remote Internet server.ResultsMedian heart rate (-0.30 to 1.10 b∙min-1) and respiratory rate (-1.25 to 0.39 br∙min-1) measurement biases were small, yet statistically significant (all P≤.003) due to the large number of observations. Measurement reliability was generally excellent (rho=.87-.97, all P<.001; intraclass correlation coefficient [ICC]=.94-.98, all P<.001; coefficient of variation [CV]=2.24-7.94%), although respiratory rate measurement reliability was poor among AF participants (rho=.43, P<.001; ICC=.55, P<.001; CV=16.61%). Data loss was minimal (<5%) when all system components were active; however, instability of the network hosting the remote data capture server resulted in data loss at the remote Internet server during some trials.ConclusionsSystem validity was sufficient for remote monitoring of heart and respiratory rates across a range of exercise intensities. Remote exercise monitoring has potential to augment current exCR and heart rate control management approaches by enabling the provision of individually tailored care to individuals outside traditional clinical environments.

Oestrus detection in dairy cows from activity and lying data using on-line individual models