The objective was to verify possible modifications of the soil structure and the pattern of the spectral response of pasture vegetation cover to animal trampling. The study was carried out on a farm in the Agreste region of Pernambuco, Brazil in an area with continuous grazing by heifers. Soil samples were collected at 36 regular points, before and after the grazing period, where the physical properties of the soil were determined at a 0.00—0.10 m depth. Before and after grazing, images of the Sentinel-2A satellite were also obtained to observe the pasture vegetation response pattern over time through Vegetation Indexes. The soil attribute data were submitted to multivariate factorial analysis. The vegetation index maps were evaluated for spatial variability. The results showed that after the grazing, there was a significant change in soil attributes and pasture, which can indicate possible degradation processes.

The aim of this study was to employ the principal component technique to physiological data and environmental thermohygrometric variables correlated with detection of clinical and subclinical mastitis in dairy cattle. A total of 24 lactating Girolando cows with different clinical conditions were selected (healthy, and with clinical or subclinical mastitis). The following physiological variables were recorded: udder surface temperature, ST (°C); eyeball temperature, ET (°C); rectum temperature, RT (°C); respiratory frequency, RF (mov. min-1). Thermohygrometric variables included air temperature, AirT (°C), and relative humidity, RU (%). ST was determined by means of thermal images, with four images per animal, on these quarters: front left side (FL), front right side (FR), rear right side (RR) and rear left side (RL), totaling 96 images. Exploratory data analysis was run through multivariate statistical technique with the employment of principal components, comprehending nine variables: ST on the FL, FR, RL and RR quarters; ET, RT; RF, AirT and RU. The representative quarters of the animals with clinical and subclinical mastitis showed udder temperatures 8.55 and 2.46° C higher than those of healthy animals, respectively. The ETs of the animals with subclinical and clinical mastitis were, respectively, 7.9 and 8.0% higher than those of healthy animals. Rectum temperatures were 2.9% (subclinical mastitis) and 5.5% (clinical mastitis) higher compared to those of healthy animals. Respiratory frequencies were 40.3% (subclinical mastitis) and 61.6% (clinical mastitis) higher compared to those of healthy animals. The first component explained 91% of the total variance for the variables analyzed. The principal component technique allowed verifying the variables correlated with the animals' clinical condition and the degree of dependence between the study variables.

Mastitis is a disease that is considered an obstacle in dairy farming. Some methods of diagnosing mastitis have been used effectively over the years, but with an associated relative cost that reduces the producer’s profit. In this context, this sector needs tools that offer an early, safe, and non-invasive diagnosis and that direct the producer to apply resources to confirm the clinical picture, minimizing the cost of monitoring the herd. The objective of this study was to develop a predictive methodology based on sequential knowledge transfer for the automatic detection of bovine subclinical mastitis using computer vision. The image bank used in this research consisted of 165 images, each with a resolution of 360 × 360 pixels, sourced from a database of 55 animals diagnosed with subclinical mastitis, all of which were not exhibiting clinical symptoms at the time of imaging. The images utilized in the sequential learning transfer were those of MammoTherm, which is used for the detection of breast cancer in women. The optimized model demonstrated the most optimal network performance, achieving 92.1% accuracy, in comparison to the model with manual search (86.1%). The proposed predictive methodologies, based on knowledge transfer, were effective in accurately classifying the images. This significantly enhanced the automatic detection of both healthy animals and those diagnosed with subclinical mastitis using thermal images of the udders of dairy cows.

The objective of this research was to assess the spatial variability pattern concerning udder surface temperature in dairy cows that were healthy and in those with mastitis. A total of 24 animals were selected - eight healthy, eight with subclinical mastitis, and eight with clinical mastitis. Images were taken with a Flir i60 thermographic camera - resolution of 0.01°C, focal length of 1.0 m, and emissivity adjusted to 0.98 - between 05:00 and 07:00, totaling 96 images, three per animal, of the front and rear, right and left mammary quarters. Analyses were run through geostatistics, with semivariogram adjustment to validate the theoretical model and build kriging maps. The average surface temperature of the mammary quarters with positive classification for subclinical mastitis stood between 33.2 ± 0.67ºC and 34.64± 1.07ºC; for negative quarters, values ranged from 29.3 ± 1.78ºC to 32.24 ± 0.62ºC. The udder surface temperatures of healthy animals were lower than those of animals with subclinical mastitis (29.3ºC ± 1.78 and 31.58ºC ± 0.62). The udder surface temperature of animals with clinical mastitis was higher, between 34.0 and 37.5°C, compared to the other clinical statuses. The scale adopted for image pattern analysis successfully identified the spatial dependence of udder surface temperature, which helped standardize diagnostic procedures for healthy animals, and for those with subclinical and clinical mastitis, by means of geostatistics.

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.