Customized Tracking Algorithm for Robust Cattle Detection and Tracking in Occlusion Environments.

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.

Cow detection and tracking system utilizing multi-feature tracking algorithm.

In modern cattle farm management systems, video-based monitoring has become important in analyzing the high-level behavior of cattle for monitoring their health and predicting calving for providing timely assistance. Conventionally, sensors have been used for detecting and tracking their activities. As the body-attached sensors cause stress, video cameras can be used as an alternative. However, identifying and tracking individual cattle can be difficult, especially for black and brown varieties that are so similar in appearance. Therefore, we propose a new method of using video cameras for recognizing cattle and tracking their whereabouts. In our approach, we applied a combination of deep learning and image processing techniques to build a robust system. The proposed system processes images in separate stages, namely data pre-processing, cow detection, and cow tracking. Cow detection is performed using a popular instance segmentation network. In the cow tracking stage, for successively associating each cow with the corresponding one in the next frame, we employed the following three features: cow location, appearance features, as well as recent features of the cow region. In doing so, we simply exploited the distance between two gravity center locations of the cow regions. As color and texture suitably define the appearance of an object, we analyze the most appropriate color space to extract color moment features and use a Co-occurrence Matrix (CM) for textural representation. Deep features are extracted from recent cow images using a Convolutional Neural Network (CNN features) and are also jointly applied in the tracking process to boost system performance. We also proposed a robust Multiple Object Tracking (MOT) algorithm for cow tracking by employing multiple features from the cow region. The experimental results proved that our proposed system could handle the problems of MOT and produce reliable performance.

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.

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.

Theileria-free grazing of dairy heifers on grassland in Kyushu, Japan where T. orientalis was epidemic before a 7-year vacancy.

This study aimed to produce a Theileria-free grazing system for Holstein heifers reared on a dairy farm in the Hita area, Kyushu, Japan. In the grazing area, spreading of T. orientalis infection was confirmed in 2009. To eradicate the T. orientalis infection, four measures were conducted: 1) 7-year deferred grazing; 2) grazing only T. orientalis-uninfected heifers; 3) anemia check by red blood cell parameters at least once per month; and 4) protecting heifers from blood-sucking T. orientalis-infected ticks. Grazing was restarted in 2017 in the same area and continued to 2021. During last 2 years of pasturing (2020-2021) all of the 129 heifers were confirmed to be T. orientalis-free. In summary, it is possible to establish a T. orientalis-free grazing system by conducting appropriate measures.

Eimeria pragensis infection alters the gut microenvironment to favor extrinsic shiga toxin-producing Escherichia coli O157:H7 colonization in mice.

We examined the effects of Eimeria pragensis infection on intestinal peristalsis, goblet cell proliferation and intestinal flora in C57BL/6 mice. Intestinal peristalsis was evaluated by radiography using barium at 7 days post-infection (p.i.). The intestinal peristalsis of E. pragensis-infected mice was significantly suppressed compared with uninfected control mice. Twenty-three mice were divided into 5 groups of 4 or 5 mice each; 2 groups of mice were infected with E. pragensis and the others were kept uninfected. At 7 days p.i., E. pragensis-infected and -uninfected mice were sacrificed to examine goblet cell numbers in the intestines, and significant decreases were observed only in the infected mice. Shiga toxin-producing Escherichia coli (STEC) O157:H7 was inoculated orally in mice both infected and uninfected with E. pragensis at 7 days p.i., with the remaining mice used as uninoculated controls. When mice were sacrificed at 2 days after STEC inoculation, STEC was only detected in the intestines of E. pragensis-infected mice. Colonization of STEC was also confirmed by immunohistochemistry on the surface of epithelial cells in concurrently infected/inoculated mice. Also, an overgrowth of residential E. coli was observed only in E. pragensis-infected mice. These results suggest that E. pragensis induces the suppression of intestinal peristalsis and modifies the intestinal environment to facilitate artificially introduced STEC colonization and multiplication, in addition to residential E. coli overgrowth.

An Absorbing Markov Chain Model to Predict Dairy Cow Calving Time.

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.

Effects of Theileria orientalis Infection on Health Status and Productivity of Dairy Cows Reared inside Barns.

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.

Activity-Integrated Hidden Markov Model to Predict Calving Time.

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.

A large-scale survey of Theileria orientalis infection in grazing dairy heifers in Kyushu, Japan.

The objective of the present study was to investigate the prevalence of Theileria orientalis infection and the influence of this disease on dairy grazing heifers in Kyushu by monitoring red blood cell (RBC) indexes, and to evaluate the efficacy of diminazene diaceturate treatment of T. orientalis-infected animals. A monthly epidemiological survey was conducted for Holstein heifers, which were reared from 10 to 16 months of age on a large commercial dairy farm and grazed on eight independent grasslands from April to November, 2009. During the survey, a total of 2,803 blood samples were collected from the 891 grazing heifers, in which the prevalence of T. orientalis infection was 52.4%. Compared with the heifers before infection, heifers with high parasitemia (more than 100 parasites per 104 RBC) had significantly decreased RBC indexes, such as RBC count, hemoglobin concentration, and hematocrit (P<0.05), whereas heifers with low parasitemia (less than 100 parasites per 104 RBC) had similar RBC indexes as those before infection. Treatment with diminazene diaceturate had lower efficacy in heifers with high parasitemia than those with low parasitemia (40.7% and 73.2% became negative, respectively, P<0.05). In summary, T. orientalis infection is a potentially serious problem in Kyushu, and it is important to routinely implement control programs for heifers that are grazed on grasslands in this region.

Epidemiological study to investigate the incidence and prevalence of clinical mastitis, peracute mastitis, metabolic disorders and peripartum disorders, on a dairy farm in a temperate zone in Japan.

BACKGROUND: Our aim was to investigate the incidence and prevalence of clinical mastitis, peracute mastitis, metabolic disorders, and peripartum disorders, and to examine factors affecting the prevalence of each disease in cows raised on a large dairy farm in a temperate climate in Japan. The present study was performed on a large commercial dairy farm with approximately 2500 Holstein cows. Data were collected from 2014 to 2018, and involved 9663 calving records for 4256 cows. RESULTS: The incidence rate on the farm was 21.9% for clinical mastitis, 10.4% for peracute mastitis, 2.9% for metabolic disorders, and 3.2% for peripartum disorders. The prevalence rates for clinical mastitis, peracute mastitis, metabolic disorders, and peripartum disorders were 28.0, 13.3, 3.7, and 4.0%, respectively. In all four diseases, the probability of time to occurrence for each disease was associated with parity and calving season (P < 0.05). Regarding metabolic disorders and peripartum disorders, the probability of occurrence decreased during the first 10 days after calving. CONCLUSIONS: Our results showed that clinical mastitis occurred most often in this temperate zone, and that metabolic disorders and peripartum disorders occurred from calving to day 10 post-calving.

Body Condition Score Estimation Based on Regression Analysis Using a 3D Camera.

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.

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.

Genetic characteristics of feral Misaki horses based on polymorphisms of microsatellites and mitochondrial DNA.

The Misaki horse is a Japanese native horse, known as the "feral horse of Cape Toi". In this study, we acquired the genetic information to establish their studbook, and analyzed their genetic characteristics for conservation. We genotyped 32 microsatellites and a mitochondrial D-loop region in 77 Misaki horses (80.2% of the population). The average number of alleles, observed heterozygosity, and expected heterozygosity were 3.4, 0.509, and 0.497, respectively. A neighbor-joining phylogenetic tree of individuals was constructed. Moreover, the results suggested that Misaki horses experienced a bottleneck, but it was neither severe nor recent. In addition, three mitochondrial haplotypes were confirmed. Consequently, we clarified the genetic background of Misaki horses that have been resident at Cape Toi for a long time.

Exploration of factors determining milk production by Holstein cows raised on a dairy farm in a temperate climate area.

In this study, we examined factors that affected milk production by cows raised in a temperate climate area. We conducted this study on a large dairy farm containing approximately 2000 Holstein cows, located in a temperate climate area. We collected 7803 calving records for 4069 cows from 2012 to 2016. We then assessed the effect of hot weather on milk yield by examining three climate factors: season, maximum temperature (MAX), and the temperature and humidity index (THI). We found that increases in heat stress caused linear decreases in milk yield (P < 0.05). Additionally, the effects of the three climate factors on milk yield varied depending on cow parity and days open (P < 0.05). Thus, management procedures should consider cow parity and lactating stage to minimize the negative effects of heat stress on milk production. We also found that the lowest Akaike information criterion value was obtained in our model when using THI for 305-day milk yield. This suggests that THI is a more accurate variable for evaluating heat stress than MAX or season.

Hepatoprotective immune response during Trichinella spiralis infection in mice.

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.

Evaluation of the natural vertical transmission of Theileria orientalis.

Bovine theileriosis, caused by Theileria orientalis, is endemic from East Asia to Oceania. Even though the disease is mainly transmitted by Haemaphysalis ticks, the T. orientalis parasite can also be transmitted vertically. To develop proper control measures, the frequency of each transmission route must be elucidated. However, the frequency of vertical transmission, including transplacental transmission, of T. orientalis in naturally infected cattle is still controversial. This study aimed to clarify the frequency of the vertical transmission of T. orientalis in naturally infected cattle. Blood samples were collected from 204 T. orientalis-infected dams and their 211 newborn calves (including 7 sets of twins) within the first 24 h as well as 30 days after birth. Furthermore, 31 and 24 calves born to T. orientalis-infected and uninfected dams, respectively, were continuously surveyed for infection until 5 months of age. A total of 5 (2.4%) dams were diagnosed with mild anemia, whereas most of the dams were asymptomatic based on hematological examination and clinical signs. PCR analysis was performed on whole blood to determine the presence of T. orientalis in calves, and no calves were PCR positive 0 and 30 days after birth. However, 9.6% and 0% of the calves born to T. orientalis-infected and uninfected dams, respectively, tested positive at 3 and 5 months of age. The sampled calves were fed in-house, and the survey was conducted during the cold season; thus, horizontal transmission through blood-sucking insects rarely occurred. Therefore, the vertical transmission of T. orientalis took as long as 3 months to become detectable by PCR and occurred in approximately 10% of field cattle.

First intermediate hosts of Paragonimus spp. in Vietnam and identification of intramolluscan stages of different Paragonimus species.

BACKGROUND: Members of the genus Paragonimus require at least three hosts in their life-cycles. The obligatory first intermediate hosts are freshwater snails. In Vietnam, although seven Paragonimus species have been recorded, the natural first intermediate hosts of almost all species have not been confirmed. The aim of this study was, therefore, to investigate snail hosts of Paragonimus species in Vietnam, and to identify Paragonimus species at intramolluscan stages. METHODS: Freshwater snails were collected from streams in Yen Bai and Quang Tri Provinces, where high prevalences of Paragonimus metacercariae in crab hosts have been reported. Snails were morphologically identified and then examined individually for Paragonimus cercariae using shedding and crushing methods. Chaetomicrocercous cercariae, the morphological class to which Paragonimus cercariae belong, were collected for morphological description and molecular species identification by analyses of ITS2 sequences. The infected snail species were identified based on analyses of nucleotide sequences of the cox1 gene. RESULTS: Three snail species were found to be infected with Paragonimus cercariae at low infection rates, ranging between 0.07-1.0%. The molecular analyses identified them as Sulcospira quangtriensis and 2 species of subfamily Triculinae. In a phylogenetic tree, these two triculine snails were related to the genera Gammatricula and Tricula with low posterior probabilities. Thus we named them as Triculinae sp. 1 and Triculinae sp. 2. Cercariae from the three snail species, Sulcospira quangtriensis, Triculinae sp. 1 and Triculinae sp. 2, were molecularly identified as Paragonimus westermani, P. heterotremus and P. proliferus, respectively. The cercariae of the three species are morphologically similar to each other, but their daughter rediae can be distinguished by the length of the intestine and the number of cercariae per redia. The rediae of P. westermani have a long intestine and each contain 6-8 cercariae. In contrast, those of P. heterotremus and P. proliferus have a short intestine and each redia contain 10-12 and 5-6 cercariae, respectively. CONCLUSIONS: Three snail species, Sulcospira quangtriensis, Triculinae sp. 1 and Triculinae sp. 2, serve as the first intermediate hosts of P. westermani, P. heterotremus and P. proliferus, respectively, in Vietnam. The length of the intestine of rediae and the number of cercariae per redia are valuable characteristics for distinguishing between larvae of these Paragonimus species.

New hematological key for bovine leukemia virus-infected Japanese Black cattle.

The European Community's (EC) Key, which is also called Bendixen's Key, is a well-established bovine leukemia virus (BLV) diagnostic method that classifies cattle according to the absolute lymphocyte count and age. The EC Key was originally designed for dairy cattle and is not necessarily suitable for Japanese Black (JB) beef cattle. This study revealed the lymphocyte counts in the BLV-free and -infected JB cattle were significantly lower than those in the Holstein cattle. Therefore, applying the EC Key to JB cattle could result in a large number of undetected BLV-infected cattle. Our proposed hematological key, which was designed for JB cattle, improves the detection of BLV-infected cattle by approximately 20%. We believe that this study could help promote BLV control.

Intrauterine infection with bovine leukemia virus in pregnant dam with high viral load.

Enzootic bovine leukemia is caused by the bovine leukemia virus (BLV). BLV is transmitted vertically or horizontally through the transfer of infected cells via direct contact, through milk, insect bites and contaminated iatrogenic procedures. However, we lacked direct evidence of intrauterine infection. The purpose of this study was to confirm intrauterine BLV infection in two pregnant dams with high viral load by cesarean delivery. BLV was detected in cord and placental blood, and the BLV in the newborns showed 100% nucleotide identity with the BLV-env sequence from the dams. Notably, a newborn was seropositive for BLV but had no colostral antibodies. In this study, we presented a direct evidence of intrauterine BLV transmission in pregnant dam with a high proviral load. These results could aid the development of BLV control measures targeting viral load.

Development of LAMP assays for the molecular detection of taeniid infection in canine in Tibetan rural area.

For field-identification of taeniid cestodes in canine animals in Tibetan area, loop-mediated isothermal amplification (LAMP) assays for Echinococcus multilocularis, E. shiquicus, Taenia hydatigena, T. multiceps, T. pisiformis and T. crassiceps were developed and evaluated along with the reported assay for E. granulosus. The LAMP assays showed specific reaction with their corresponding target species DNA with the detection limit of 1 to 10 pg. Moreover, the assays for E. granulosus, E. multilocularis, T. hydatigena and T. multiceps could detect DNA extracted from 3 or more eggs of their corresponding target species. Then, the LAMP assays were applied on samples containing 3 to 35 taeniid eggs obtained from 61 field-collected canine feces in Qinghai, and the result was compared with a reported multiplex PCR and sequence analysis. The LAMP assays and the PCR detected single species DNA of E. granulosus, E. shiquicus, T. hydatigena and T. multiceps in 5, 2, 44 and 2 samples, respectively. In the rest 8 samples, DNA of both E. granulosus and T. hydatigena were detected by the PCR but the LAMP assays detected those DNAs in 2 samples and only T. hydatigena DNA in 6 samples. It was assumed that less than 3 E. granulosus eggs were mixed in the samples although the samples contained 21 to 27 eggs in total. In conclusion, the LAMP assays were less sensitive than the multiplex PCR, but would have adequate sensitivity for field use in Tibetan area.