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.

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 β-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 β-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).

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.

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.

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.