Cow key point detection in indoor housing conditions with a deep learning model.

Lameness in dairy cattle is a costly and highly prevalent problem that affects all aspects of sustainable dairy production, including animal welfare. Automation of gait assessment would allow monitoring of locomotion in which the cows' walking patterns can be evaluated frequently and with limited labor. With the right interpretation algorithms, this could result in more timely detection of locomotion problems. This in turn would facilitate timely intervention and early treatment, which is crucial to reduce the effect of abnormal behavior and pain on animal welfare. Gait features of dairy cows can potentially be derived from key points that locate crucial anatomical points on a cow's body. The aim of this study is 2-fold: (1) to demonstrate automation of the detection of dairy cows' key points in a practical indoor setting with natural occlusions from gates and races, and (2) to propose the necessary steps to postprocess these key points to make them suitable for subsequent gait feature calculations. Both the automated detection of key points as well as the postprocessing of them are crucial prerequisites for camera-based automated locomotion monitoring in a real farm environment. Side-view video footage of 34 Holstein-Friesian dairy cows, captured when exiting the milking parlor, were used for model development. From these videos, 758 samples of 2 successive frames were extracted. A previously developed deep learning model called T-LEAP was trained to detect 17 key points on cows in our indoor farm environment with natural occlusions. To this end, the dataset of 758 samples was randomly split into a train (n = 22 cows; no. of samples = 388), validation (n = 7 cows; no. of samples = 108), and test dataset (n = 15 cows; no. of samples = 262). The performance of T-LEAP to automatically assign key points in our indoor situation was assessed using the average percentage of correctly detected key points using a threshold of 0.2 of the head length (PCKh0.2). The model's performance on the test set achieved a good result with PCKh0.2: 89% on all 17 key points together. Detecting key points on the back (n = 3 key points) of the cow had the poorest performance PCKh0.2: 59%. In addition to the indoor performance of the model, a more detailed study of the detection performance was conducted to formulate postprocessing steps necessary to use these key points for gait feature calculations and subsequent automated locomotion monitoring. This detailed study included the evaluation of the detection performance in multiple directions. This study revealed that the performance of the key points on a cows' back were the poorest in the horizontal direction. Based on this more in-depth study, we recommend the implementation of the outlined postprocessing techniques to address the following issues: (1) correcting camera distortion, (2) rectifying erroneous key point detection, and (3) establishing the necessary procedures for translating hoof key points into gait features.

Comparison of three mathematical models to estimate lactation performance in dairy cows.

Milk yield dynamics and production performance reflect how dairy cows cope with their environment. To optimize farm management, time-series of individual cow milk yield have been studied in the context of precision livestock farming, and many mathematical models have been proposed to translate raw data into useful information for the stakeholders of the dairy chain. To gain better insights on the topic, this study aimed at comparing 3 recent methods that allow to estimate individual cow potential lactation performance, using daily data recorded by the automatic milking systems of 14 dairy farms (7 Holstein, 7 Italian Simmental) from Belgium, the Netherlands, and Italy. An iterative Wood model (IW), a perturbed lactation model (PLM), and a quantile regression (QR) were compared in terms of estimated total unperturbed (i.e., expected) milk production and estimated total milk loss (relative to unperturbed yield). The IW and PLM can also be used to identify perturbations of the lactation curve and were thus compared in this regard. The outcome of this study may help a given end-user in choosing the most appropriate method according to their specific requirements. If there is a specific interest in the post-peak lactation phase, IW can be the best option. If one wants to accurately describe the perturbations of the lactation curve, PLM can be the most suitable method. If there is need for a fast and easy approach on a very large data set, QR can be the choice. Finally, as an example of application, PLM was used to analyze the effect of cow parity, calving season, and breed on their estimated lactation performance.

Milk yield residuals and their link with the metabolic status of dairy cows in the transition period.

The transition period is one of the most challenging periods in the lactation cycle of high-yielding dairy cows. It is commonly known to be associated with diminished animal welfare and economic performance of dairy farms. The development of data-driven health monitoring tools based on on-farm available milk yield development has shown potential in identifying health-perturbing events. As proof of principle, we explored the association of these milk yield residuals with the metabolic status of cows during the transition period. Over 2 yr, 117 transition periods from 99 multiparous Holstein-Friesian cows were monitored intensively. Pre- and postpartum dry matter intake was measured and blood samples were taken at regular intervals to determine ß-hydroxybutyrate, nonesterified fatty acids (NEFA), insulin, glucose, fructosamine, and IGF1 concentrations. The expected milk yield in the current transition period was predicted with 2 previously developed models (nextMILK and SLMYP) using low-frequency test-day (TD) data and high-frequency milk meter (MM) data from the animal's previous lactation, respectively. The expected milk yield was subtracted from the actual production to calculate the milk yield residuals in the transition period (MRT) for both TD and MM data, yielding MRTTD and MRTMM. When the MRT is negative, the realized milk yield is lower than the predicted milk yield, in contrast, when positive, the realized milk yield exceeded the predicted milk yield. First, blood plasma analytes, dry matter intake, and MRT were compared between clinically diseased and nonclinically diseased transitions. MRTTD and MRTMM, postpartum dry matter intake and IGF1 were significantly lower for clinically diseased versus nonclinically diseased transitions, whereas ß-hydroxybutyrate and NEFA concentrations were significantly higher. Next, linear models were used to link the MRTTD and MRTMM of the nonclinically diseased cows with the dry matter intake measurements and blood plasma analytes. After variable selection, a final model was constructed for MRTTD and MRTMM, resulting in an adjusted R2 of 0.47 and 0.73, respectively. While both final models were not identical the retained variables were similar and yielded comparable importance and direction. In summary, the most informative variables in these linear models were the dry matter intake postpartum and the lactation number. Moreover, in both models, lower and thus also more negative MRT were linked with lower dry matter intake and increasing lactation number. In the case of an increasing dry matter intake, MRTTD was positively associated with NEFA concentrations. Furthermore, IGF1, glucose, and insulin explained a significant part of the MRT. Results of the present study suggest that milk yield residuals at the start of a new lactation are indicative of the health and metabolic status of transitioning dairy cows in support of the development of a health monitoring tool. Future field studies including a higher number of cows from multiple herds are needed to validate these findings.

Sensor-based behavioral patterns can identify heat-sensitive lactating dairy cows.

Heat stress impairs the health and performance of dairy cows, yet only a few studies have investigated the diversity of cattle behavioral responses to heat waves. This research was conducted on an Italian Holstein dairy farm equipped with precision livestock farming sensors to assess potential different behavioral patterns of the animals. Three heat waves, defined as at least five consecutive days with mean daily temperature-humidity index higher than 72, were recorded in the farm area during the summer of 2021. Individual daily milk yield data of 102 cows were used to identify "heat-sensitive" animals, meaning the cows that, under a given heat wave, experienced a milk yield drop that was not linked with other health events (e.g., mastitis). Milk yield drops were detected as perturbations of the lactation curve estimated by iteratively using Wood's equation. Individual daily minutes of lying, chewing, and activity were retrieved from ear-tag-based accelerometer sensors. Semi-parametric generalized estimating equations models were used to assess behavioral deviations of heat-sensitive cows from the herd means under heat stress conditions. Heat waves were associated with an overall increase in the herd's chewing and activity times, along with an overall decrease of lying time. Heat-sensitive cows spent approximately 15 min/days more chewing and performing activities (p < 0.05). The findings of this research suggest that the information provided by high-frequency sensor data could assist farmers in identifying cows for which personalized interventions to alleviate heat stress are needed.

Milk losses and dynamics during perturbations in dairy cows differ with parity and lactation stage.

Milk yield dynamics during perturbations reflect how cows respond to challenges. This study investigated the characteristics of 62,406 perturbations from 16,604 lactation curves of dairy cows milked with an automated milking system at 50 Belgian, Dutch, and English farms. The unperturbed lactation curve representing the theoretical milk yield dynamics was estimated with an iterative procedure fitting a model on the daily milk yield data that was not part of a perturbation. Perturbations were defined as periods of at least 5 d of negative residuals having at least 1 day that the total daily milk production was below 80% of the estimated unperturbed lactation curve. Every perturbation was characterized and split in a development and a recovery phase. Based hereon, we calculated both the characteristics of the perturbation as a whole, and the duration, slopes, and milk losses in the phases separately. A 2-way ANOVA followed by a pairwise comparison of group means was carried out to detect differences between these characteristics in different lactation stages (early, mid-early, mid-late, and late) and parities (first, second, and third or higher). On average, 3.8 ± 1.9 (mean ± standard deviation) perturbations were detected per lactation in the first 305 d after calving, corresponding to an estimated 92.1 ± 135.8 kg of milk loss. Only 1% of the lactations had no perturbations. On average, 2.3 kg of milk was lost per day in the development phase, while the recovery phase corresponded to an average increase in milk production of 1.5 kg/d, and these phases lasted an average of 10.1 and 11.6 d, respectively. Perturbation characteristics were significantly different across parity and lactation stage groups, and early and mid-early perturbations in higher parities were found to be more severe with faster development rates, slower recovery rates, and higher milk losses. The method to characterize perturbations can be used for precision phenotyping purposes that look into the response of cows to challenges or that monitor applications (e.g., to evaluate the development and recovery of diseases and how these are affected by preventive actions or treatments).

Productive life span and resilience rank can be predicted from on-farm first-parity sensor time series but not using a common equation across farms.

A dairy cow's lifetime resilience and her ability to recalve gain importance on dairy farms, as they affect all aspects of the sustainability of the dairy industry. Many modern farms today have milk meters and activity sensors that accurately measure yield and activity at a high frequency for monitoring purposes. We hypothesized that these same sensors can be used for precision phenotyping of complex traits such as lifetime resilience or productive life span. The objective of this study was to investigate whether lifetime resilience and productive life span of dairy cows can be predicted using sensor-derived proxies of first-parity sensor data. We used a data set from 27 Belgian and British dairy farms with an automated milking system containing at least 5 yr of successive measurements. All of these farms had milk meter data available, and 13 of these farms were also equipped with activity sensors. This subset was used to investigate the added value of activity meters to improve the model's prediction accuracy. To rank cows for lifetime resilience, a score was attributed to each cow based on her number of calvings, her 305-d milk yield, her age at first calving, her calving intervals, and the DIM at the moment of culling, taking her entire lifetime into account. Next, this lifetime resilience score was used to rank the cows within their herd, resulting in a lifetime resilience ranking. Based on this ranking, cows were classified in a low (last third), moderate (middle third), or high (first third) resilience category within farm. In total, 45 biologically sound sensor features were defined from the time series data, including measures of variability, lactation curve shape, milk yield perturbations, activity spikes indicating estrous events, and activity dynamics representing health events (e.g., drops in daily activity). These features, calculated on first-lactation data, were used to predict the lifetime resilience rank and, thus, to predict the classification within the herd (low, moderate, or high). Using a specific linear regression model progressively including features stepwise selected at farm level (cutoff P-value of 0.2), classification performances were between 35.9 and 70.0% (46.7 ± 8.0, mean ± SD) for milk yield features only, and between 46.7 and 84.0% (55.5 ± 12.1, mean ± SD) for lactation and activity features together. This is, respectively, 13.7 and 22.2% higher than what random classification would give. Moreover, using these individual farm models, only 3.5 and 2.3% of cows were classified high when they were actually low, or vice versa, whereas respectively 91.8 and 94.1% of wrongly classified animals were predicted in an adjacent category. The sensor features retained in the prediction equation of the individual farms differed across farms, which demonstrates the variability in culling and management strategies across farms and within farms over time. This lack of a common model structure across farms suggests the need to consider local (and evidence-based) culling management rules when developing decision support tools for dairy farms. With this study we showed the potential of precision phenotyping of complex traits based on biologically meaningful features derived from readily available sensor data. We conclude that first-lactation milk and activity sensor data have the potential to predict cows' lifetime resilience rankings within farms but that consistency between farms is currently lacking.

Prediction of first test day milk yield using historical records in dairy cows.

The transition between two lactations remains one of the most critical periods during the productive life of dairy cows. In this study, we aimed to develop a model that predicts the milk yield of dairy cows from test day milk yield data collected in the previous lactation. In the past, data routinely collected in the context of herd improvement programmes on dairy farms have been used to provide insights in the health status of animals or for genetic evaluations. Typically, only data from the current lactation is used, comparing expected (i.e., unperturbed) with realised milk yields. This approach cannot be used to monitor the transition period due to the lack of unperturbed milk yields at the start of a lactation. For multiparous cows, an opportunity lies in the use of data from the previous lactation to predict the expected production of the next one. We developed a methodology to predict the first test day milk yield after calving using information from the previous lactation. To this end, three random forest models (nextMILKFULL, nextMILKPH, and nextMILKP) were trained with three different feature sets to forecast the milk yield on the first test day of the next lactation. To evaluate the added value of using a machine-learning approach against simple models based on contemporary animals or production in the previous lactation, we compared the nextMILK models with four benchmark models. The nextMILK models had an RMSE ranging from 6.08 to 6.24 kg of milk. In conclusion, the nextMILK models had a better prediction performance compared to the benchmark models. Application-wise, the proposed methodology could be part of a monitoring tool tailored towards the transition period. Future research should focus on validation of the developed methodology within such tool.

Study of folliculogenesis in vivo in guinea pig.

Understanding normal folliculogenesis in guinea pigs is fundamental as a first step towards the development of a guinea pig follicle culture system. The aims of this study were (1) to characterise morphological changes during follicular development in vivo and (2) to describe the growth pattern of follicles. Cycling guinea pigs were infused with 5-bromo-2'-deoxyuridine for 1 or 2 weeks and sacrificed at time points ranging from 0 to 37 days after the infusion. The granulosa cell number in the largest cross-sections increased from 25.0+/-6.1 (mean+/-S.D.) in primary (type 2) to 192.0+/-65.9 in preantral (type 5) and 256.3+/-96.9 in antral (type 6) follicles. The oocyte diameter increased from 44.8+/-6.2 microm (type 2) to 72.8+/-9.1 microm (type 5) and 78.9+/-9.3 microm (type 6) and the follicle diameter from 67.9+/-10.1 microm (type 2) to 188.9+/-29.7 microm (type 5) and 231.0+/-56.1 microm (type 6). After a 1-week labelling period, about 71% of type 2 follicles had at least one labelled granulosa cell, as did 95% of type 3-4, and 100% of type 5 and 6. About 1 week was needed to achieve 95% mitotic activity in granulosa cells (GC) of type 5 and 6 follicles, while about 2 weeks was required to achieve 100% mitotic activity in GC of type 3-4 and more than 2 weeks for GC of type 2 follicles. These data provide some baselines for the examination of a guinea pig follicle culture system.

Competitive inhibition assay for the detection of progesterone in dairy milk using a fiber optic SPR biosensor.

Analytical methods that are often used for the quantification of progesterone in bovine milk include immunoassays and chromatographic techniques. Depending on the selected method, the main disadvantages are the cost, time-to-result, labor intensity and usability as an automated at-line device. This paper reports for the first time on a robust and practical method to quantify small molecules, such as progesterone, in complex biological samples using an automated fiber optic surface plasmon resonance (FO-SPR) biosensor. A FO-SPR competitive inhibition assay was developed to determine biologically relevant concentrations of progesterone in bovine milk (1-10 ng/mL), after optimizing the immobilization of progesterone-bovine serum albumin (P4-BSA) conjugate, the specific detection with anti-progesterone antibody and the signal amplification with goat anti-mouse gold nanoparticles (GAM-Au NPs). The progesterone was detected in a bovine milk sample with minimal sample preparation, namely ½ dilution of the sample. Furthermore, the developed bioassay was benchmarked against a commercially available ELISA, showing excellent agreement (R2 = 0.95). Therefore, it is concluded that the automated FO-SPR platform can combine the advantages of the different existing methods for quantification of progesterone: sensitivity, accuracy, cost, time-to-result and ease-of-use.

Melatonin has dose-dependent effects on folliculogenesis, oocyte maturation capacity and steroidogenesis.

Chemo and/or radiotherapy applied to young cancer patients most often have severe effects upon female fertility. Today, few options are available to protect ovarian function in females. However, these options are either ineffective, belong to the field of experimental research or/and are not applicable to all patients. Drugs that could protect the oocyte and its surrounding feeder cells from damage can be of great importance. Melatonin, being an important indirect antioxidant and a powerful direct free radical scavenger could be such a reagent. This paper reports the direct effects of different melatonin concentrations (range: 1 nM to 2 mM) on folliculogenesis and oogenesis of in vitro cultured mouse ovarian follicles. Early secondary mouse follicles were cultured in vitro for 12 days under different melatonin regimes. Every fourth day, survival rates were scored, follicles were morphologically evaluated and medium was collected for steroid analyses. On day 12, in vitro ovulation was induced by hCG/EGF. Eighteen hours later, oocytes were measured, oocyte maturation was evaluated and normality of spindle and chromosomes ascertained. Results obtained in this study indicated that 2mM melatonin is toxic. One mM negatively influenced oocyte maturation capacity. In the presence of 100 microM melatonin, androstenedione and progesterone were increased whereas estradiol was not influenced. Lower melatonin concentrations had no effect on the evaluated parameters. These data indicate an effect of melatonin on theca cell steroidogenesis. For prophylactic use, a dose of 10 microM could be suitable to reduce oxidative stress in cultured follicles.

Measuring the drinking behaviour of individual pigs housed in group using radio frequency identification (RFID).

Changes in the drinking behaviour of pigs may indicate health, welfare or productivity problems. Automated monitoring and analysis of drinking behaviour could allow problems to be detected, thus improving farm productivity. A high frequency radio frequency identification (HF RFID) system was designed to register the drinking behaviour of individual pigs. HF RFID antennas were placed around four nipple drinkers and connected to a reader via a multiplexer. A total of 55 growing-finishing pigs were fitted with radio frequency identification (RFID) ear tags, one in each ear. RFID-based drinking visits were created from the RFID registrations using a bout criterion and a minimum and maximum duration criterion. The HF RFID system was successfully validated by comparing RFID-based visits with visual observations and flow meter measurements based on visit overlap. Sensitivity was at least 92%, specificity 93%, precision 90% and accuracy 93%. RFID-based drinking duration had a high correlation with observed drinking duration (R 2=0.88) and water usage (R 2=0.71). The number of registrations after applying the visit criteria had an even higher correlation with the same two variables (R 2=0.90 and 0.75, respectively). There was also a correlation between number of RFID visits and number of observed visits (R 2=0.84). The system provides good quality information about the drinking behaviour of individual pigs. As health or other problems affect the pigs' drinking behaviour, analysis of the RFID data could allow problems to be detected and signalled to the farmer. This information can help to improve the productivity and economics of the farm as well as the health and welfare of the pigs.

Cytogenetic studies in mouse oocytes irradiated in vitro at different stages of maturation, by use of an early preantral follicle culture system.

In vivo studies on X-irradiated mice have shown that structural chromosome aberrations can be induced in female germ cells and that the radiation-induced chromosomal damage strongly depends on the stage of maturation reached by the oocytes at the time of irradiation. In the present study, the sensitivity of oocytes to induction of chromosome damage by radiation was evaluated at two different stages, by use of a recently developed method of in vitro culture covering a crucial period of follicle/oocyte growth and maturation. A key feature of this system is that growth and development of all follicles is perfectly synchronized, due to the selection of a narrow class of follicles in the start-off culture. This allows irradiation of well-characterized and homogenous populations of follicles, in contrast to the situation prevailing in vivo. Follicles were X-irradiated with either 2 or 4 Gy, on day 0 of culture (early preantral follicles with one to two cell layers) or on day 12, 3h after hormonal stimulation of ovulation (antral Graafian follicles). Ovulated oocytes, blocked in metaphase I (MI) by colchicine, were fixed 16 h after hormonal stimulation and analyzed for chromosome aberrations. The results confirm the high radiosensitivity of oocytes at 2 weeks prior to ovulation and the even higher radiosensitivity of those irradiated a few hours before ovulation, underlining the suitability of the in vitro system for further studies on the genetic effects of ionising radiation in female mammals.

The current knowledge on radiosensitivity of ovarian follicle development stages.

BACKGROUND: The aim of this paper is to review the available information on ovarian radiation sensitivity and the genetic hazard of ionizing radiation in female mammals including humans. METHODS: The literature present in the author's laboratories (international papers from the 1970s) was complemented by a Medline literature search using the keywords 'ionizing radiation genetic effects', 'oocyte radiosensitivity' and 'oocyte DNA repair' (1990-2008). Further articles were acquired from citations in the research papers and reports. RESULTS: Animal data show that oocyte radiosensitivity varies widely according to the follicle/oocyte stage and the species. Oocytes near ovulation show the highest susceptibility to radiation induction of mutational events. Congenital anomalies have been observed after exposure to high doses (1-5 Gy), but extrapolation of these data to humans requires caution. In humans, the dose required to induce permanent ovarian failure would vary from 20.3 Gy at birth to 14.3 Gy at 30 years. Most epidemiological studies found little evidence of genetic diseases at the doses at which medical, occupational or accidental exposure occurred. CONCLUSIONS: The fact that genetic effects were observed in irradiated animals suggests that these could also occur in humans. The probability of such events remains low compared with the 'spontaneous' risks of genetic effects.

Differential FSH exposure in preantral follicle culture has marked effects on folliculogenesis and oocyte developmental competence.

BACKGROUND: We investigated at what stage early cultured preantral mouse follicles become dependent on a minimal effective FSH dose (10 mIU/ml) and analysed the influence of implementing FSH at several time-points during in vitro culture. METHODS: Two-layered mouse follicles were cultured for 12 days under seven different FSH exposure regimens and ovulated on day 13 by hCG/EGF. Ovulated cumulus-oocyte complexes were fertilized and embryos were cultured up to the blastocyst stage. RESULTS: When FSH was absent or added only once at the start of culture, follicle survival was significantly reduced (22 and 52% respectively versus 95% when FSH was continuously present, P < 0.01) and oocyte quality was compromised, providing few oocytes for embryo culture (19 and 7% versus 71% in continuous presence of FSH, P < 0.01). Optimal follicle survival rates can be ensured by implementing FSH at the latest from day 4 of culture. By introducing FSH later than day 4, follicle survival rates and number of ovulated oocytes decreased. Estradiol production and luteinization were strongly related to the moment of introducing FSH in culture. Fertilization and preimplantation embryo development rate were found to be highest in cultures where FSH support was implemented during the preantral stage. CONCLUSION: Exposure to FSH before formation of the antral-like cavity had a positive effect on follicle survival and oocyte robustness.