This study presents a bibliometric review aimed at mapping and analyzing the scientific literature related to the ethical implications of artificial intelligence (AI) in livestock farming, which is a rapidly emerging yet still underexplored field in international research. Based on the Scopus database, 151 documents published between 2015 and 2025 were identified and analyzed using the VOSviewer version 1.6.20 and Biblioshiny for Bibliometrix (RStudio version 2023.12.1) tools. The results show a significant increase in publications from 2021 onwards, reflecting the growing maturity of discussions around the integration of digital technologies in the agricultural sector. Keyword co-occurrence and bibliographic coupling analyses revealed the formation of four main thematic clusters, covering technical applications in precision livestock farming as well as reflections on governance, animal welfare, and algorithmic justice. The most influential authors, high-impact journals, and leading countries in the field were also identified. As a key contribution, this study highlights the lack of robust ethical guidelines and proposes future research directions for the development of regulatory frameworks, codes of conduct, and interdisciplinary approaches. The findings underscore the importance of aligning technological innovation with ethical responsibility and social inclusion in the transition to digital livestock farming.

This paper explores the potential of infrared thermography and geostatistics in animal production and presents the results of the application of the combination of these techniques, contributing significantly to efforts to obtain animals’ responses to the environments in which they are located and thereby ensuring improvements in productivity and animal welfare. The objective was to verify the variability in surface temperature in pigs submitted to different climate control systems using geostatistics. Three growing animals per stall were selected. Dry bulb temperature (Tbd, °C), relative humidity (RH, %) and thermal images were recorded at 08:00 and 12:00 h. To analyze the data, semivariograms were made, the theoretical model was validated and kriging maps were constructed. The mean temperature of the pigs in the pen with adiabatic evaporative cooling (AEC) ranged from 32.40 to 36.25 °C; for the pigs in the forced ventilation (FV) pen, the range of variation was from 32.51 to 36.81 °C. In the control group (Con), with natural ventilation, the average temperature was 37.51 to 38.45 °C. The geostatistical analysis provided a mathematical model capable of illustrating the variation in temperature in the caudal–dorsal regions of the pigs according to the environments to which the animals were subjected.

Heat stress stands out as one of the main elements linked to concerns related to animal thermal comfort. This research aims to develop a sequential methodology for the extraction of automatic characteristics from thermal images and the classification of heat stress in pigs by means of machine learning. Infrared images were obtained from 18 pigs housed in air-conditioned and non-air-conditioned pens. The image analysis consisted of its pre-processing, followed by color segmentation to isolate the region of interest and later the extraction of the animal’s surface temperatures, from a developed algorithm and later the recognition of the comfort pattern through machine learning. The results indicated that the automated color segmentation method was able to identify the region of interest with an average accuracy of 88% and the temperature extraction differed from the Therma Cam program by 0.82 °C. Using a Vector Support Machine (SVM), the research achieved an accuracy rate of 80% in the automatic classification of pigs in comfort and thermal discomfort, with an accuracy of 91%, indicating that the proposal has the potential to monitor and evaluate the thermal comfort of pigs effectively.

A expansão urbana desalinhada ao planejamento ambiental resulta em alterações sensíveisà qualidade de vida da população, geram microclimas específicos que podem ocasionar o desconforto térmico.Nesse sentido, objetivou-se nessa pesquisa estudar a dinâmica do sistema climático urbano do bairro de Dois de Irmãos a partir da análise do campo térmico e de suas relações com o uso e a ocupação do solo. A área de estudo compreende Campus Recife da Universidade Federal Rural de Pernambuco – UFRPE, localizado no bairro de Dois Irmãos na cidade do Recife/PE. A metodologia foi conduzida com base nas seguintes etapas; mapeamento do uso e ocupação; instalação de sensor tipo Hobo U-Series Data Logger; coleta de parâmetros de temperatura e umidade relativa do ar , e por fim realização o cálculo do nível de conforto térmico (IDT). Verificou-se que a variação do índice de desconforto térmico entre os pontos estudados foram de 0,36 °C e 0,31 °C nos períodos seco e chuvoso, respectivamente. E a comparação entre os valores médios das variáveis analisadas registrou uma redução de 0,59 °C na temperatura do ar e acréscimo de 2,84% na umidade relativa e, consequentemente, numa redução de 0,91 °C no Índice de Desconforto de Thom. Conclui-se que ambos os pontos apresentaram um desconforto térmico, ainda que apresentevasta quantidade de áreas verdes, ainda é deve-se implantar estratégias de amenização climática. Esse estudo subsidiar o plano diretor do Campus. DOI: https://doi.org/10.5281/zenodo.3522477

Observing pig behavior, such as feed intake, water intake, and resting behavior, is essential for improving the well-being of these animals. However, monitoring such behaviors by traditional methods can be exhausting for both humans and animals, interfering with their development. The research aimed to identify behavioral patterns of pigs in an air-conditioned environment through computer vision. Microcameras were installed in the animals’ stalls to generate videos over an experimental period of 92 days and the temperature and humidity of the air were simultaneously recorded. The physiological variables of the animals were collected to identify whether they were under heat stress. To recognize the drinking, eating, standing and lying behavior of pigs, YOLOv5 was trained and then the model was used to detect the animals. Regions in the images corresponding to the feeders and drinkers were established. To identify feeding behavior and water intake, criteria based on the occupation of the feeding zone by pigs detected in the standing position were established. The results showed that the trained model achieved an average accuracy rate of 97.3% and an average recall of 96.1% in animal detection. The model exhibited 97.5% accuracy and 97.0% recall rates in recognizing the feeding behavior and water consumption of pigs. The proposed method can be used in videos or images and minimizes the need for manual intervention, offering an efficient means of monitoring pig behavior in agricultural environments and contributing to the productivity of pig farming operations.

This study applied geostatistics to analyze thermal images of the back surface of pigs’ ears (TSO) to understand how spatial temperature variability influences thermoregulation. The objective was to assess TSO variability in pigs housed under two climate control systems, namely, pens without cooling (BTEST) and with an evaporative cooling system (BECS), using infrared thermography and geostatistical tools. A total of 432 thermal images were obtained from 18 finishing pigs at 08:00, 12:00, and 16:00. Semivariograms were modeled and validated, and kriging maps were developed to visualize the spatial temperature distribution. The pens were thermally characterized using reclassified Temperature and Humidity Index (THI) values. The Gaussian model (R² > 0.9) showed strong spatial dependence in temperature data. Pigs in the BECS system exhibited lower average TSO temperatures (28.2–38.6 °C) than those in the BTEST system, where temperatures exceeded 34 °C, highlighting the role of cooling in mitigating heat stress. In both systems, higher THI values were associated with increased TSO, indicating thermal discomfort under elevated environmental temperatures. Geostatistical analysis effectively revealed spatial patterns and variability in surface temperatures, providing key insights into how environmental conditions impact pigs’ thermal responses.