Yoichiro Horii

Ensuring precise calving time prediction necessitates the adoption of an automatic and precisely accurate cattle tracking system. Nowadays, cattle tracking can be challenging due to the complexity of their environment and the potential for missed or false detections. Most existing deep-learning tracking algorithms face challenges when dealing with track-ID switch cases caused by cattle occlusion. To address these concerns, the proposed research endeavors to create an automatic cattle detection and tracking system by leveraging the remarkable capabilities of Detectron2 while embedding tailored modifications to make it even more effective and efficient for a variety of applications. Additionally, the study conducts a comprehensive comparison of eight distinct deep-learning tracking algorithms, with the objective of identifying the most optimal algorithm for achieving precise and efficient individual cattle tracking. This research focuses on tackling occlusion conditions and track-ID increment cases for miss detection. Through a comparison of various tracking algorithms, we discovered that Detectron2, coupled with our customized tracking algorithm (CTA), achieves 99% in detecting and tracking individual cows for handling occlusion challenges. Our algorithm stands out by successfully overcoming the challenges of miss detection and occlusion problems, making it highly reliable even during extended periods in a crowded calving pen.

The objective of the present study was to investigate the associations between major diseases (clinical mastitis, peracute mastitis, metabolic disorders, peripartum disorders) and four parameters related to productivity (305-day milk yield, number of days open, culling rate, death rate) on a large dairy farm in a temperate zone with approximately 2500 Holstein cows. Data were collected from 2014 to 2018 and involved 9663 calving records for 4256 cows. We found negative effects of clinical mastitis, peracute mastitis, metabolic disorders, and peripartum disorders on the productivity of cows. Clinical-mastitis-suffered cows with multiple diseases had more days open compared with those with clinical mastitis alone and the healthy group, and they had a higher death rate than the healthy group, whereas there was no difference in death rate between the clinical mastitis only and healthy groups. Cows suffering from peracute mastitis, metabolic disorders, and peripartum disorders with either single or multiple diseases exhibited reduced productivity compared with the healthy group. Our findings clearly show that major diseases of cows in a temperate zone have severely negative effects on their productivity.

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.

Abnormal behavioral changes in the regular daily mobility routine of a pregnant dairy cow can be an indicator or early sign to recognize when a calving event is imminent. Image processing technology and statistical approaches can be effectively used to achieve a more accurate result in predicting the time of calving. We hypothesize that data collected using a 360-degree camera to monitor cows before and during calving can be used to establish the daily activities of individual pregnant cows and to detect changes in their routine. In this study, we develop an augmented Markov chain model to predict calving time and better understand associated behavior. The objective of this study is to determine the feasibility of this calving time prediction system by adapting a simple Markov model for use on a typical dairy cow dataset. This augmented absorbing Markov chain model is based on a behavior embedded transient Markov chain model for characterizing cow behavior patterns during the 48 h before calving and to predict the expected time of calving. In developing the model, we started with an embedded four-state Markov chain model, and then augmented that model by adding calving as both a transient state, and an absorbing state. Then, using this model, we derive (1) the probability of calving at 2 h intervals after a reference point, and (2) the expected time of calving, using their motions between the different transient states. Finally, we present some experimental results for the performance of this model on the dairy farm compared with other machine learning techniques, showing that the proposed method is promising.

The objective of the present study was to investigate the effects of Theileria orientalis on the severity of anemia, the prevalence of disease within 21 days after calving and productivity in cows raised inside barns. This longitudinal observational study, which was conducted on a commercial dairy farm in Japan, involved 627 Holstein cows subjected to PCR analysis for T. orientalis. In study 1, we collected blood samples from 156 sick cows within 21 days after calving, and we found the prevalence of T. orientalis infection to be 65.4%. In study 2, we randomly selected 471 cows during the dry period and collected blood samples to conduct PCR analysis for T. orientalis and determined the prevalence of T. orientalis infection to be 69.0%. Compared with the values for the T. orientalis-uninfected group, the T. orientalis-infected cows had significantly decreased hemoglobin concentrations and hematocrit, but there were no differences in the other complete blood count indexes between the two groups. In addition, there were no differences in productivity and the prevalence of major diseases between the T. orientalis-infected and uninfected cows. In summary, T. orientalis had few effects on anemia, productivity and the health of cows raised inside a barn.

Accurately predicting when calving will occur can provide great value in managing a dairy farm since it provides personnel with the ability to determine whether assistance is necessary. Not providing such assistance when necessary could prolong the calving process, negatively affecting the health of both mother cow and calf. Such prolongation could lead to multiple illnesses. Calving is one of the most critical situations for cows during the production cycle. A precise video-monitoring system for cows can provide early detection of difficulties or health problems, and facilitates timely and appropriate human intervention. In this paper, we propose an integrated approach for predicting when calving will occur by combining behavioral activities extracted from recorded video sequences with a Hidden Markov Model. Specifically, two sub-systems comprise our proposed system: (i) Behaviors extraction such as lying, standing, number of changing positions between lying down and standing up, and other significant activities, such as holding up the tail, and turning the head to the side; and, (ii) using an integrated Hidden Markov Model to predict when calving will occur. The experiments using our proposed system were conducted at a large dairy farm in Oita Prefecture in Japan. Experimental results show that the proposed method has promise in practical applications. In particular, we found that the high frequency of posture changes has played a central role in accurately predicting the time of calving.

The Body Condition Score (BCS) for cows indicates their energy reserves, the scoring for which ranges from very thin to overweight. These measurements are especially useful during calving, as well as early lactation. Achieving a correct BCS helps avoid calving difficulties, losses and other health problems. Although BCS can be rated by experts, it is time-consuming and often inconsistent when performed by different experts. Therefore, the aim of our system is to develop a computerized system to reduce inconsistencies and to provide a time-saving solution. In our proposed system, the automatic body condition scoring system is introduced by using a 3D camera, image processing techniques and regression models. The experimental data were collected on a rotary parlor milking station on a large-scale dairy farm in Japan. The system includes an application platform for automatic image selection as a primary step, which was developed for smart monitoring of individual cows on large-scale farms. Moreover, two analytical models are proposed in two regions of interest (ROI) by extracting 3D surface roughness parameters. By applying the extracted parameters in mathematical equations, the BCS is automatically evaluated based on measurements of model accuracy, with one of the two models achieving a mean absolute percentage error (MAPE) of 3.9%, and a mean absolute error (MAE) of 0.13.

Infections with gastrointestinal nematodes provoke immune and inflammatory responses mediated by cytokines released from T-helper type-2 (Th2) cells. Infections with Trichinella species have been reported to differ by the host species. Previously, in rats, we observed acute liver inflammation in response to infection with Trichinella spiralis, and the rat hosts showed a series of biochemical changes characterized by a decrease in serum paraoxonase (PON)1 activity associated with the down-regulation of hepatic PON1 synthesis. In the present study, we investigated the effect(s) of species differences on the immune response against T. spiralis infection by analyzing serum PON1 activity and the associated inflammatory/anti-inflammatory mediators in mice. There were inconsistent changes in the serum PON1 activity of mice infected with T. spiralis, and these changes were associated with significant increases in the serum levels of interleukin (IL)-2, IL-4, IL-10, IL-12 (p70), granulocyte-macrophage colony-stimulating factor, and tumor necrosis factor α during the enteric phase of the infection, while the levels of IL-5 and interferon γ were significantly increased throughout the entire experimental period. Moreover, T. spiralis infection in mice was associated with little inflammatory cell infiltration in hepatic tissues. Given the zoonotic prevalence of T. spiralis, further mechanistic research in this area is warranted.