Effect of Major Diseases on Productivity of a Large Dairy Farm in a Temperate Zone in Japan

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

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

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