The use of artificial intelligence techniques with advanced computer vision techniques offers great potential for non-invasive health assessments in the poultry industry. Evaluating the condition of poultry by monitoring their droppings can be highly valuable as significant changes in consistency and color can be indicators of serious and infectious diseases. While most studies have prioritized the classification of droppings into two categories (normal and abnormal), with some relevant studies dealing with up to five categories, this investigation goes a step further by employing image processing algorithms to categorize droppings into six classes, based on visual information indicating some level of abnormality. To ensure a diverse dataset, data were collected in three different poultry farms in Lithuania by capturing droppings on different types of litter. With the implementation of deep learning, the object detection rate reached 92.41% accuracy. A range of machine learning algorithms, including different deep learning architectures, has been explored and, based on the obtained results, we have proposed a comprehensive solution by combining different models for segmentation and classification purposes. The results revealed that the segmentation task achieved the highest accuracy of 0.88 in terms of the Dice coefficient employing the K-means algorithm. Meanwhile, YOLOv5 demonstrated the highest classification accuracy, achieving an ACC of 91.78%.

Digital technologies that continuously quantify animal behavior, physiology, and production offer significant potential for the early identification of health and welfare disorders of dairy cows. In this study, a multimodal artificial intelligence (AI) framework is proposed for real-time health monitoring of dairy cows through the integration of physiological, behavioral, production, and thermal imaging data, targeting veterinarian-confirmed udder, leg, and hoof infections. Predictions are generated at the cow-day level by aggregating multimodal measurements collected during daily milking events. The dataset comprised 88 lactating cows, including veterinarian-confirmed udder, leg, and hoof infections grouped under a single ‘sick’ label. To prevent information leakage, model evaluation was performed using a cow-level data split, ensuring that data from the same animal did not appear in both training and testing sets. The system is designed to detect early deviations from normal health trajectories prior to the appearance of overt clinical symptoms. All measurements, with the exception of the intra-ruminal bolus sensor, were obtained non-invasively within a commercial dairy farm equipped with automated milking and monitoring infrastructure. A key novelty of this work is the simultaneous integration of data from three independent sources: an automated milking system, a thermal imaging camera, and an intra-ruminal bolus sensor. A hybrid deep learning architecture is introduced that combines the core components of established models, including U-Net, O-Net, and ResNet, to exploit their complementary strengths for the analysis of dairy cow health states. The proposed multimodal approach achieved an overall accuracy of 91.62% and an AUC of 0.94 and improved classification performance by up to 3% compared with single-modality models, demonstrating enhanced robustness and sensitivity to early-stage disease.

The fundamental differences between high- and low-frequency ultrasound for medical purposes were demonstrated. A model describing the effect of ultrasound on erythrocyte aggregation was developed, and the rapid movement of erythrocytes toward the nodes of a standing acoustic wave was demonstrated, with its velocity compared to the rate of erythrocyte dissociation under the influence of shear forces. The t-test was used to assess the statistical significance of differences between two blood samples and confirmed the effect of low-frequency ultrasound intensity on erythrocyte aggregation. The study employed a patented low-frequency ultrasound transducer generating a traveling acoustic wave that produces shear forces capable of disrupting erythrocyte aggregates into individual erythrocytes. Since the developed technique is intended for human therapy, it is assumed that the proposed low-frequency ultrasound parameters are safe for flowing blood. Due to deeper and more precise penetration of the acoustic signal into tissues, this ultrasound transducer may be promising for improving microcirculation and alleviating patient condition without medication, as well as for reducing blood pressure and heart rate. The developed technique also enables more effective disruption of heart valve plaques and shows therapeutic potential for tumor treatment and in vivo drug encapsulation. Since erythrocytes in diabetic patients are stiffer than those in healthy individuals, their passage through capillaries is more difficult. Therefore, the developed and patented ultrasound-based sole stimulation technique may produce a positive physiological effect by stimulating blood flow in the capillaries of patients with foot ulcers.