Estrous detection by monitoring ventral tail base surface temperature using a wearable wireless sensor in cattle

Capturing temperature changes on the ocular surface along with estrus and ovulation using infrared thermography in Japanese Black cows

Pre-ovulatory follicles are cooler than the neighboring reproductive organs in cows. Thus, measuring the temperature of reproductive organs could be a useful method for predicting estrus and ovulation in cows, and the establishment of a non-invasive technique is required. In this study, we used infrared thermography (IRT) to measure ocular surface temperature as a potential surrogate for reproductive organ temperature. Five Japanese Black cows with synchronized estrus were subjected to temperature measurements in five regions of the ocular surface, including the nasal conjunctiva, nasal limbus, center cornea, temporal limbus, and temporal conjunctiva, twice a day (0800 h and 1600 h) during the experimental period. The temperatures in the five regions significantly declined in cows from estrus to ovulation. To the best of our knowledge, this study is the first to use IRT to show a temperature decrease in the ocular surface along with estrus to ovulation in Japanese Black cows.

Technological Tools and Artificial Intelligence in Estrus Detection of Sows-A Comprehensive Review

In animal farming, timely estrus detection and prediction of the best moment for insemination is crucial. Traditional sow estrus detection depends on the expertise of a farm attendant which can be inconsistent, time-consuming, and labor-intensive. Attempts and trials in developing and implementing technological tools to detect estrus have been explored by researchers. The objective of this review is to assess the automatic methods of estrus recognition in operation for sows and point out their strong and weak points to assist in developing new and improved detection systems. Real-time methods using body and vulvar temperature, posture recognition, and activity measurements show higher precision. Incorporating artificial intelligence with multiple estrus-related parameters is expected to enhance accuracy. Further development of new systems relies mostly upon the improved algorithm and accurate data provided. Future systems should be designed to minimize the misclassification rate, so better detection is achieved.

Monitoring ventral tail base surface temperature for fever detection in calves

In this study, we investigated whether monitoring the ventral tail base surface temperature (ST) using a wearable wireless sensor could be effective for fever detection in calves with experimentally induced pneumonia after inoculation with Histophilus somni strain 2336. We found a significant difference in the changes in ST values between the control and H. somni-inoculated groups after 24 h of inoculation and detected fever; however, the rectal temperature showed a significant difference between the groups after 12 h of inoculation. When a significant difference in the ST between the two groups was observed, serum haptoglobin concentration and exacerbation of clinical score increased in the H. somni-inoculated group compared with those in the control group. Pneumonia was observed in the H. somni-inoculated group at necropsy, indicating that the changes in ST may reflect fever with inflammation caused by H. somni infection. Our results demonstrated that monitoring ST using a sensor attached to the ventral tail base can detect fever in calves and may be a useful and labor-saving tool for the health management of calves.

Digital infrared thermal imaging of udder skin surface temperature: a novel non-invasive technology to monitor calving process in Murrah buffalo (Bubalus bubalis)

Quantifiable decline in the maternal body temperature during the pre-calving offers the possibilities for predicting the calving that can improve the calving management. As infrared thermography (IRT) is a simple non-contact tool for precise measurement of surface temperature, we investigated the use of IRT to establish thermal signatures around calving in the Murrah buffalo. The IRT of eye, right lateral, left lateral and rear side of udder skin surface temperature (USST) were recorded at 6 h interval from 96 h before the expected date of calving, at the time of calving and 24 h post-calving in Murrah buffaloes (n = 28). In parallel, blood samples were collected for progesterone (P4) assay. The results revealed that the IRT of the eye, right and left lateral and rear side of USST showed a significant decrease in the temperature from 48 h pre-calving till the onset of calving with a ΔT (°C) of 0.56, 0.91, 0.70, and 0.90, respectively. Mean USST significantly declined from 48 h pre-calving with a ΔT of 0.85 °C. The residual temperature of both eye and various ROI of the udder also followed a similar and significant declining trend from 48 to 0 h of calving indicating that circadian influence on the USST was minimum. Plasma P4 concentration significantly decreased from 72 h pre-calving till calving. It is concluded that a marked reduction in the IRT of the USST at 6-12 h pre-calving would be useful in predicting the onset of calving in the Murrah buffalo.

Infrared Thermography as a Potential Non-Invasive Tool for Estrus Detection in Cattle and Buffaloes

The productivity of dairy animals has significantly increased over the past few decades due to intense genetic selection. However, the enhanced yield performance of milk animals caused a proportional increase in stress and compromised reproductive efficiency. Optimal reproductive performance is mandatory for the sustainable production of dairy animals. Reproductive efficiency is marked by proper estrus detection and precise breeding to achieve maximum pregnancies. The existing conventional methods of estrus detection are somewhat labor intensive and less efficient. Similarly, the modern automated methods that rely on detecting physical activity are expensive, and their efficiency is affected by factors such as type of housing (tie stall), flooring, and environment. Infrared thermography has recently emerged as a technique that does not depend on monitoring physical activity. Furthermore, infrared thermography is a non-invasive, user-friendly, and stress-free option that aids in the detection of estrus in dairy animals. Infrared thermography has the potential to be considered a useful non-invasive tool for detecting temperature fluctuations to generate estrus alerts without physical contact in cattle and buffaloes. This manuscript highlights the potential use of infrared thermography to understand reproductive physiology and practical implementation of this technique through discussing its advantages, limitations, and possible precautions.

Assessment of Ventral Tail Base Surface Temperature for the Early Detection of Japanese Black Calves with Fever

The objective in the present study was to assess the ventral tail base surface temperature (ST) for the early detection of Japanese Black calves with fever. This study collected data from a backgrounding operation in Miyazaki, Japan, that included 153 calves aged 3-4 months. A wearable wireless ST sensor was attached to the surface of the ventral tail base of each calf at its introduction to the farm. The ventral tail base ST was measured every 10 min for one month. The present study conducted an experiment to detect calves with fever using the estimated residual ST (rST), calculated as the estimated rST minus the mean estimated rST for the same time on the previous 3 days, which was obtained using machine learning algorithms. Fever was defined as an increase of ≥1.0 °C for the estimated rST of a calf for 4 consecutive hours. The machine learning algorithm that applied was a random forest, and 15 features were included. The variable importance scores that represented the most important predictors for the detection of calves with fever were the minimum and maximum values during the last 3 h and the difference between the current value and 24- and 48-h minimum. For this prediction model, accuracy, precision, and sensitivity were 98.8%, 72.1%, and 88.1%, respectively. The present study indicated that the early detection of calves with fever can be predicted by monitoring the ventral tail base ST using a wearable wireless sensor.

Prediction of 24-h and 6-h Periods before Calving Using a Multimodal Tail-Attached Device Equipped with a Thermistor and 3-Axis Accelerometer through Supervised Machine Learning

Simple Summary

Routine visual observation of signs of imminent calving, such as softening of ligaments around the tailhead and udder distension, is time-consuming, and the resulting calving predictions are relatively unreliable. To address this issue, we used a multimodal tail-attached device (tail sensor) and developed calving prediction models through supervised machine learning. The tail sensor is equipped with a thermistor and 3-axis accelerometer, and can monitor tail skin temperature, activity intensity, lying time, posture changes (standing to lying or vice versa), and tail raising behavior. Using the sensor data with a non-sensor-based data (days to the expected calving date), we developed calving prediction models for 24-h and 6-h periods before calving and evaluated their predictive ability under two distinct housing conditions, tethering (tie-stall) and untethering (free-stall and individual pen). Our results demonstrated that calving prediction models based on tail sensor data with supervised machine learning have the potential to achieve effective calving prediction, irrespective of the cattle housing conditions.

Abstract

In this study, we developed calving prediction models for 24-h and 6-h periods before calving using data on physiological (tail skin temperature) and behavioral (activity intensity, lying time, posture change, and tail raising) parameters obtained using a multimodal tail-attached device (tail sensor). The efficiencies of the models were validated under tethering (tie-stall) and untethering (free-stall and individual pen) conditions. Data were collected from 33 and 30 pregnant cattle under tethering and untethering conditions, respectively, from approximately 15 days before the expected calving date. Based on pre-calving changes, 40 features (8 physiological and 32 behavioral) were extracted from the sensor data, and one non-sensor-based feature (days to the expected calving date) was added to develop models using a support vector machine. Cross-validation showed that calving within the next 24 h under tethering and untethering conditions was predicted with a sensitivity of 97% and 93% and precision of 80% and 76%, respectively, while calving within the next 6 h was predicted with a sensitivity of 91% and 90% and precision of 88% and 90%, respectively. Calving prediction models based on the tail sensor data with supervised machine learning have the potential to achieve effective calving prediction, irrespective of the cattle housing conditions.

Accuracy to Predict the Onset of Calving in Dairy Farms by Using Different Precision Livestock Farming Devices

Simple Summary

If the onset of calving can be accurately detected as well as appropriate calving assistance can be performed on a dairy farm, at that time, the prevalence of dystocia, stillbirth, vaginal laceration, retained fetal membranes, and consequent clinical metritis/endometritis can be decreased significantly. Therefore, in order to reduce these losses, our primary task must be to predict the onset of calving accurately and provide timely and professional calving assistance. This review focuses on the diagnostic possibilities and limitations of detecting the onset calving in the field.

Abstract

Besides traditional methods such as evaluation of the external preparatory and behavioral signs, which even presently are widely used also in large dairy farms, there are several new possibilities such as measuring body (intravaginal, ventral tail-base surface, ear surface, or reticulo-ruminal) temperature, detecting behavioral signs (rumination, eating, activity, tail raising) or detecting the expulsion of the device inserted into the vagina or fixed to the skin of the vulva when allantochorion appears in the vulva to predict the onset of the second stage of calving. Presently none of the single sensors or a combination of sensors can predict the onset of calving with acceptable accuracy. At the same time, with the exception of the iVET^® birth monitoring system, not only the imminent onset of calving could be predicted with high accuracy, but a significantly lower prevalence rate of dystocia, stillbirth, retained fetal membranes, uterine diseases/clinical metritis could be reached while calving-to-conception interval was significantly shorter compared with the control groups. These results may confirm the use of these devices in dairy farms by allowing appropriate intervention during calving when needed. In this way, we can reduce the negative effect of dystocia on calves and their dams and improve their welfare.

Positive relationship of rectal temperature at fixed timed artificial insemination on pregnancy outcomes in beef cattle

The overarching aim was to examine the relationship of rectal temperature at fixed time artificial insemination (FTAI) on pregnancy outcomes in a typical breeding season with expected pregnancy rates approaching 50% using Bos indicus and Bos taurus cattle. This represents a continuum of steps to test the hypothesis that elevated body temperature at or around insemination is functionally important to maximize pregnancy outcomes. Rectal temperature of Bos indicus cattle at FTAI ranged from 37.0 to 40.9 °C; 60.6% were hyperthermic. Positive factors impacting pregnancy outcomes were rectal temperature at FTAI, body condition, and estrus patch scores. Rectal temperature at FTAI was positively associated with pregnancy outcomes (P < 0.0001); per each 1 °C increase pregnancy odds increased 1.9 times (95% CI: 1.4 to 2.6). Highest pregnancy outcomes occurred with rectal temperatures exceeding 40 °C (P = 0.0004). Rectal temperature before FTAI in Bos taurus cattle ranged from 37.8 to 41.8 °C; 43.3% were hyperthermic. Factors impacting pregnancy were rectal temperature at FTAI, estrus activity, parity, and ambient conditions on day of FTAI. Rectal temperature of Bos taurus cattle at FTAI was positively associated with pregnancy (P = 0.0286); odds increased 1.45 times (95% CI: 1.0 to 2.0) per each 1 °C increase. Highest pregnancy outcomes occurred with rectal temperatures at FTAI exceeding 40 °C (P = 0.057). Moreover, positive relationship of rectal temperature at FTAI to pregnancy persisted in estrual females (71.25% of total; P = 0.0408; OR 1.5; 95% CI: 1.0 to 2.2). Mindful that 1) elevated temperatures observed in Bos indicus and Bos taurus cattle directly promote meiotic resumption of the oocyte in vitro and that 2) in vivo hyperthermia alters intrafollicular components which others have shown to potentiate ovulation and promote meiotic resumption, it is biologically plausible that an acute elevation in body temperature at or around time of insemination is functionally important to maximize pregnancy outcomes.

Analysis of Gut Microbiota and Metabolites in Diannan Small Ear Sows at Diestrus and Metestrus

The physiological state of the host affects the gut microbes. The estrus cycle is critical to the reproductive cycle of sows. However, the association between gut microbes and animal estrus is poorly understood. Here, high-throughput 16S rRNA sequencing and liquid chromatography-mass spectrometry (LC-MS) non-targeted metabolome technology were used to study the estrous cycles in Diannan small ear pigs. Significantly different gut microbiota and metabolites of sows at estrous and diestrus were screened out and the correlation was analyzed. We found that the intestinal microbial composition and microbial metabolism of Diannan small ear sows were significantly different at diestrus and metestrus. The abundances of Spirochaetes, Spirochaetia, Spirochaetales, Spirochaetaceae, Deltaproteobacteria, unidentified_Alphaproteobacteria, Ruminococcus_sp_YE281, and Treponema_berlinense in intestinal microorganisms of Diannan small ear sows at metestrus are significantly higher than that at diestrus. Propionic acid, benzyl butyrate, sucrose, piperidine, and 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR) were significantly enriched at metestrus compared with diestrus, which were involved in the energy metabolism-related pathways and activated protein kinase (AMPK) signaling pathway. At diestrus and metestrus, differential microbiota of unidentified_Alphaproteobacteria, Intestinimonas, Peptococcus, Terrisporobacter, and differential metabolites of piperidine, propionic acid, and benzyl butyrate, sucrose, 4-methyl catechol, and AICAR exist a certain degree of correlation. Therefore, unidentified_Alphaproteobacteria, Ruminococcus_sp_YE281, and Treponema_berlinense may have a potential role at metestrus of the Diannan small ear sows. AICAR may be apotential marker of estrus Diannan small ear sows feces, but further studies about the specific mechanism are needed. These findings provide a new perspective for sows production management and improving sows reproductive performance.

Recent Advances on Early Detection of Heat Strain in Dairy Cows Using Animal-Based Indicators: A Review

In pursuit of precision livestock farming, the real-time measurement for heat strain-related data has been more and more valued. Efforts have been made recently to use more sensitive physiological indicators with the hope to better inform decision-making in heat abatement in dairy farms. To get an insight into the early detection of heat strain in dairy cows, the present review focuses on the recent efforts developing early detection methods of heat strain in dairy cows based on body temperatures and respiratory dynamics. For every candidate animal-based indicator, state-of-the-art measurement methods and existing thresholds were summarized. Body surface temperature and respiration rate were concluded to be the best early indicators of heat strain due to their high feasibility of measurement and sensitivity to heat stress. Future studies should customize heat strain thresholds according to different internal and external factors that have an impact on the sensitivity to heat stress. Wearable devices are most promising to achieve real-time measurement in practical dairy farms. Combined with internet of things technologies, a comprehensive strategy based on both animal- and environment-based indicators is expected to increase the precision of early detection of heat strain in dairy cows.

Impact of an acute heat shock during in vitro maturation on interleukin 6 and its associated receptor component transcripts in bovine cumulus-oocyte complexes

An acute heat stress event after the LH surge increased interleukin 6 (IL6) levels in the follicular fluid of the ovulatory follicle in hyperthermic cows. To examine direct consequences of a physiologically-relevant elevated temperature (41.0°C) on the cumulus-oocyte complex (COC), IL6 transcript abundance and related receptor components were evaluated throughout in vitro maturation. Heat-induced increases in IL6 were first noted at 4 hours of in vitro maturation (hIVM); peak levels occurred at 4.67 versus 6.44 hIVM for 41.0 and 38.5°C COCs, respectively (SEM = 0.23; P < 0.001). Peak IL6ST levels occurred at 6.95 versus 8.29 hIVM for 41.0 and 38.5°C, respectively (SEM = 0.23; P < 0.01). Transcript for LIF differed over time (P < 0.0001) but was not affected by 41.0°C exposure. Blastocyst development after performing IVF was not affected by 41.0°C exposure for 4 or 6 h. When limiting analysis to when IL6 was temporally produced, progesterone levels were only impacted by time and temperature (no interaction). Heat-induced shift in the temporal production of IL6 and IL6ST along with its impact on progesterone likely cooperate in heat-induced hastening of meiotic progression described by others.

An attempt at estrus detection in cattle by continuous measurements of ventral tail base surface temperature with supervised machine learning

We aimed to determine the effectiveness of estrus detection based on continuous measurements of the ventral tail base surface temperature (ST) with supervised machine learning in cattle. ST data were obtained through 51 estrus cycles on 11 female cattle (six Holsteins and five Japanese Blacks) using the tail-attached sensor. Three estrus detection models were constructed with the training data (n = 17) using machine learning techniques (random forest, artificial neural network, and support vector machine) based on 13 features extracted from sensing data (indicative of estrus-associated ST changes). Estrus detection abilities of the three models on test data (n = 34) were not statistically different among models in terms of sensitivity and precision (range 50.0% to 58.8% and 60.6% to 73.1%, respectively). The relatively poor performance of the models might indicate the difficulty of separating estrus-associated ST changes from estrus-independent fluctuations in ST.

Rumen-Reticular Temperature During Estrus and Ovulation Using Automated Activity Monitors in Dairy Cows

This study aimed to determine changes in rumen-reticular temperature (RRT) at estrus or ovulation and determine if these changes are associated with the intensity of estrous expression. Cows were equipped with an automated activity monitor (AAM) and a rumen-reticular bolus thermometer. A total of 190 estrus episodes were used where physical activity data was recorded using the AAM and ovulation was determined via ultrasonography of the ovaries at alert and twice daily, for a maximum of 60 h. Estrous expression was assessed using the maximum activity and duration in which activity remained above the AAM threshold; both characteristics were categorized using the median. Temperature data was collected for the duration of estrus, as well as for the interval of time where ovulation was determined to occur. Three measures of temperature were calculated: (1) positive area under the curve (AUC), (2) maximum positive temperature change (PTC), (3) maximum negative temperature change (NTC) at estrus (AUC_E/ PTC_E/ NTC_E) and around ovulation (AUC_O/PTC_O/ NTC_O). Both AUC and PTC were greater during estrus than around ovulation (2.7 ± 0.2 and 1.1 ± 0.3°C² for AUC_E and AUC_O; 0.55 ± 0.03 and 0.26 ± 0.04°C for PTC_E and PTC_O, respectively). In contrast, NTC was lower around ovulation than estrus (-0.28 ± 0.05 and -0.60 ± 0.06°C for NTC_E and NTC_O). Cows with greater estrous expression had greater AUC and PTC during estrus than around ovulation, but cows with lesser estrous expression had similar AUC and PTC. Increases in AUC [High THI (Temperature Humidity Index): High activity: 4.7 ± 0.5, Low activity: 1.5 ± 0.4; Low THI: High activity: 3.1 ± 0.2, Low activity: 1.4 ± 0.2 °C²] and PTC (High THI: High activity: 0.79 ± 0.08, Low activity: 0.36 ± 0.07; Low THI: High activity: 0.60 ± 0.04, Low activity: 0.47 ± 0.04°C) associated with estrous expression were found to be greater on days with higher THI. Alerts created using standard deviations from the mean were unable to detect estrus or ovulation with acceptable precision. Further research is required to determine how changes in RRT can be used successfully to predict estrus and ovulation.

Technical note: Calving prediction in dairy cattle based on continuous measurements of ventral tail base skin temperature using supervised machine learning

In this study, we developed a calving prediction model based on continuous measurements of ventral tail base skin temperature (ST) with supervised machine learning and evaluated the predictive ability of the model in 2 dairy farms with distinct cattle management practices. The ST data were collected at 2- or 10-min intervals from 105 and 33 pregnant cattle (mean ± standard deviation: 2.2 ± 1.8 parities) reared in farms A (freestall barn, in a temperate climate) and B (tiestall barn, in a subarctic climate), respectively. After extracting maximum hourly ST, the change in values was expressed as residual ST (rST = actual hourly ST - mean ST for the same hour on the previous 3 d) and analyzed. In both farms, rST decreased in a biphasic manner before calving. Briefly, an ambient temperature-independent gradual decrease occurred from around 36 to 16 h before calving, and an ambient temperature-dependent sharp decrease occurred from around 6 h before until calving. To make a universal calving prediction model, training data were prepared from pregnant cattle under different ambient temperatures (10 data sets were randomly selected from each of the 3 ambient temperature groups: <15°C, ≥15°C to <25°C, and ≥25°C in farm A). An hourly calving prediction model was then constructed with the training data by support vector machine based on 15 features extracted from sensing data (indicative of pre-calving rST changes) and 1 feature from non-sensor-based data (days to expected calving date). When the prediction model was applied to the data that were not part of the training process, calving within the next 24 h was predicted with sensitivities and precisions of 85.3% and 71.9% in farm A (n = 75), and 81.8% and 67.5% in farm B (n = 33), respectively. No differences were observed in means and variances of intervals from the calving alerts to actual calving between farms (12.7 ± 5.8 and 13.0 ± 5.6 h in farms A and B, respectively). Above all, a calving prediction model based on continuous measurement of ST with supervised machine learning has the potential to achieve effective calving prediction, irrespective of the rearing condition in dairy cattle.

Sampling strategy and measurement device affect vaginal temperature outcomes in lactating dairy cattle

Body temperature (BT) is widely used to evaluate health and heat load status in cattle. Despite its importance, studies vary in how BT is measured and in the biological interpretation of the results. Costs, practicality, labor, and welfare concerns can affect how BT is measured, including frequency of measurement and the type of device used. Inaccurate BT outcomes may have implications for cattle welfare; for example, animals may only receive treatment when fever is identified. Our objectives were (1) to compare measurement of vaginal temperature (VT) using relatively small, inexpensive, and low-accuracy loggers (±0.5 to ±1°C, iButton range; Embedded Data Systems, Lawrenceburg, KY) to a high-accuracy logger (±0.1°C; StarOddi, Gardabaer, Iceland), and (2) to evaluate how different BT sampling strategies correspond to 24-h VT in lactating dairy cows. To address the first objective, VT data from 54 cows were recorded every 45 min for 12 d/cow, on average, using 2 different types of temperature loggers (StarOddi DST centi-T and iButton DS1921H or DS1922L) attached to a shortened, hormone-free controlled internal drug release insert. Average VT obtained from both loggers were compared using mixed models and regression analyses. In addition, we tested the consistency of the low-accuracy loggers in detecting cows with elevated BT using the kappa coefficient of concordance. To address the second objective, VT data from 20 cows were recorded every min for 9 to 11 d/cow using StarOddi loggers. Using these data, we estimated average VT using 11 sampling strategies (every 5, 10, 15, 30, 45, 60, and 120 min, 1×/d recorded in the morning or afternoon, 2×/d, or 3×/d). Estimates and observed means were compared using linear regression. Compared with StarOddi loggers, the iButtons either underestimated (H model: 38.7 vs. 38.0 ± 0.06°C) or overestimated VT (L model: 38.7 vs. 39.2 ± 0.04°C). When considering elevated or fever VT thresholds, iButtons did not correctly classify animals; kappa coefficients of concordance were ≤0.35. Measuring VT as often as every 120 min resulted in more accurate estimates compared with strategies that recorded it once to thrice per day. These results indicate that the type of device (i.e., data logger) and sampling strategies affect BT outcomes and that these decisions affect the interpretation of BT data.

Alterations in vaginal temperature during the estrous cycle in dairy cows detected by a new intravaginal device-a pilot study

Estrus identification is important in dairy cow production. At present, estrus identification is automated with a pedometer or accelerometer and the results remain unsatisfactory. It was previously reported that body temperature changes during estrus. In the present study, dairy cow vaginal temperature (VT) was monitored during various seasons, and an increase in VT of 0.3 °C was suggested for the onset of estrus, using an automated VT monitoring system developed in-house. Natural and synchronized estrus were measured simultaneously. The VT was determined to be in circadian rhythm and significantly higher in summer than in either autumn or winter (P < 0.05). VT difference (between estrus VT and average VT 7 days earlier) gradually increased, reached a peak of 0.56 °C ± 0.17 at 4 h before the end of estrus, and then decreased to the normal. The VT of cows in estrus and the duration of their estrus were significantly affected by seasons and estrus types (P < 0.05). VT gradually decreased in response to prostaglandin (PG) injection and was significantly lower (0.15-0.35 °C) from 9 to 33 h after the drug administration than the average VT at the same time 7 days earlier (P < 0.05). Changes in circadian and seasonal VT and in the estrous cycle can be monitored to assess the physiological status of cows and will help in developing an effective automated estrus identification technique. Results of this pilot study should be validated in further studies.

Early pregnancy diagnoses based on physiological indexes of dairy cattle: a review

With the development of standardization and scaling in the dairy farming industry, timely and accurate pregnancy diagnosis is required to improve the benefits of breeding by shortening the calving interval. However, the current pregnancy diagnostic methods cannot meet the requirements of the industry. Here, we review changes in the physiological indexes and in the function and morphological status of the reproductive organs of dairy cows at the early stages of pregnancy. In addition, the corresponding pregnancy diagnostic methods based on certain indexes are well development, and the pregnancy diagnostic approaches based on remote sensing and automation technology have become inevitable trends in the industry. These applications will reveal physiological regularities in pregnancy and benefit the detailed management of dairy cows during early pregnancy.

Prepartum change in ventral tail base surface temperature in beef cattle: comparison with vaginal temperature and behavior indices, and effect of ambient temperature

Prediction of parturition is essential for sustainable production in beef and dairy cattle, yet the present methods are limited by their high invasiveness and low utility. Here we compared prepartum changes in ventral tail base surface temperature (ST) with changes in vaginal temperature (VT) and behavioral indices. We analyzed 22 parturitions from 22 beef cows. Changes in daily values of ST, VT, and behavioral indices over the 7 days before parturition were investigated. Hourly values were calculated as the actual values minus the mean values for the same hour over a 3-day period, and the changes in hourly values over the 48 h before parturition were investigated. To test the effect of ambient temperature, tested cows were assigned to two season-groups based on the ambient temperature to which they were exposed (warm: n = 13; cool: n = 9), and the daily and hourly values of the indices were compared between seasons. A decrease in ST occurred approximately 30 h before parturition, which was similar to the time of the decrease in VT and earlier than the increase of behavioral indices. In addition, a unique fluctuation of ST observed in the last few hours before parturition indicates that ST could provide a sign for parturition not only in the long-term like VT, but also in the short-term like behavioral indices. Although ST was more sensitive to ambient temperature than VT or the behavioral indices, the day of parturition could be predicted from ST in both the warm and cool seasons.

WSMS: Wearable Stress Monitoring System based on IoT Multi-Sensor Platform for Living Sheep Transportation

Farming herdsmen, sheep dealers, and veterinarians are increasingly interested in continuously monitoring sheep basic physiological characteristics (such as the heart rate and skin temperature) outside the laboratory environment, with the aim of identifying the physiological links between stress, uncomfortable, excitement, and other pathological states. This paper proposes a non-invasive Wearable Stress Monitoring System (WSMS) with PhotoPlethysmoGram (PPG), Infrared Temperature Measurement (ITM), and Inertial Measurement Units (IMU) that aimed to remotely and continuously monitor the stress signs of sheep during transportation. The purpose of this study was implemented by following the multi-dimensional sensing platform to identify more pressure information. The designed WSMS showed sufficient robustness in recording and transmitting sensing data of physiology and environment during transport. The non-contact and non-destructive monitoring method that was proposed in this paper was helpful in minimizing the effects of sheep stress load.

Combination of Multi-Agent Systems and Wireless Sensor Networks for the Monitoring of Cattle

Precision breeding techniques have been widely used to optimize expenses and increase livestock yields. Notwithstanding, the joint use of heterogeneous sensors and artificial intelligence techniques for the simultaneous analysis or detection of different problems that cattle may present has not been addressed. This study arises from the necessity to obtain a technological tool that faces this state of the art limitation. As novelty, this work presents a multi-agent architecture based on virtual organizations which allows to deploy a new embedded agent model in computationally limited autonomous sensors, making use of the Platform for Automatic coNstruction of orGanizations of intElligent Agents (PANGEA). To validate the proposed platform, different studies have been performed, where parameters specific to each animal are studied, such as physical activity, temperature, estrus cycle state and the moment in which the animal goes into labor. In addition, a set of applications that allow farmers to remotely monitor the livestock have been developed.

Real Time Analysis of Bioanalytes in Healthcare, Food, Zoology and Botany

The growing demand for real time analysis of bioanalytes has spurred development in the field of wearable technology to offer non-invasive data collection at a low cost. The manufacturing processes for creating these sensing systems vary significantly by the material used, the type of sensors needed and the subject of study as well. The methods predominantly involve stretchable electronic sensors to monitor targets and transmit data mainly through flexible wires or short-range wireless communication devices. Capable of conformal contact, the application of wearable technology goes beyond the healthcare to fields of food, zoology and botany. With a brief review of wearable technology and its applications to various fields, we believe this mini review would be of interest to the reader in broad fields of materials, sensor development and areas where wearable sensors can provide data that are not available elsewhere.