## 2 Related Works  
With the rapid growth of computational power and data transfer capabilities, machine learning (ML) and artificial intelligence (AI) are also making inroads into animal husbandry and veterinarian research. In particular, IRT is being increasingly used for health monitoring and the diagnosis of dairy cows, especially in studies related to heat stresses, which causes severe losses, helping us analyze its effects on nutrition, milk production, reproduction, etc. There is plenty of evidence for the potential benefits of using IRT for monitoring under health status in dairy cows and for the early detection of mastitisitis [8; 9; 10; 11; 12; 13; 14; 15; 16]. Its role in detecting hoof lesions and lameness has also been reported [17; 18; 19]. The growth of the population and the increase of quality standards has set a requirement for the production of more and better quality food. The capabilities and potential benefits of IRT make systems for the automatic collection and processing of thermographic information and decision-making particularly important [20]. Some studies use computer vision technology and IRT to detect hoof diseases in dairy cows (see [21]). Others resort on deep learning methods, and more precisely, convolutional neural networks (CNNs), for detecting mastitis in dairy cows based on infrared images [22; 23; 24]. The possibility of using a combination of visible and infrared imaging has also been explored; for instance, to automate the measurement of pig skin surface temperature, emphasizing once more on the benefits of non-invasive methods [25]. The expansion of ML and AI into modern farming practices is facilitated by the advancement of increasingly more precise and affordable sensors reacting to subtle changes in the environment and the animals' physiological parameters. This provides new opportunities for data analysis and ML-based optimization, which is the leading idea of'smart farming'. There are many directions for development of this modern research field, but we shall focus on non-invasive diagnostics, allowing for precise real-time monitoring of the animals' health condition without disturbing their comfort and exposing them to unnecessary stress. Body temperature is one of the most important physiological parameters to keep track of, and there are abundant data for it. Hence, multi-regression models predicting the rectal temperature are quite promising for the improvement of health conditions in animal farms and their overall optimization. Although the research area is relatively new, such studies have been conducted before, using different methods [26; 27; 28; 29; 30; 31; 32]and technologies [33; 34], leading to different conclusions. We rely a lot on thermal images, like in [35; 36], but we feed them into a multimodal algorithm along with tabular features, which improves the accuracy significantly. These ML models have become popular only in recent years due to their complexity, but with modern AutoML tools, they are already a standard task for data scientists [37; 38]. The development of algorithms for processing information from multiple sensors began in the late 1980s, when the field of data fusion emerged [39]. Its aims to increase the accuracy of the assessment and reliability of the information system in case one of its sensors fails, to obtain a more accurate picture of the observed event or process, and to expand the area of observation. Multi-model approaches have become necessary in cases where the systems under evaluation may have different modes of operation (changing dynamics), each corresponding to a different model. The main advantage of this approach is its extreme robustness, which naturally comes at the cost of a higher computing load due to processing several models simultaneously. In the great variety of multi-model approaches [40], the algorithm of interacting multiple models [41] has emerged as one of the most successful.