RESUMO
Lameness affects animal mobility, causing pain and discomfort. Lameness in early stages often goes undetected due to a lack of observation, precision, and reliability. Automated and non-invasive systems offer precision and detection ease and may improve animal welfare. This study was conducted to create a repository of images and videos of sows with different locomotion scores. Our goal is to develop a computer vision model for automatically identifying specific points on the sow's body. The automatic identification and ability to track specific body areas, will allow us to conduct kinematic studies with the aim of facilitating the detection of lameness using deep learning. The video database was collected on a pig farm with a scenario built to allow filming of sows in locomotion with different lameness scores. Two stereo cameras were used to record 2D videos images. Thirteen locomotion experts assessed the videos using the Locomotion Score System developed by Zinpro Corporation. From this annotated repository, computational models were trained and tested using the open-source deep learning-based animal pose tracking framework SLEAP (Social LEAP Estimates Animal Poses). The top-performing models were constructed using the LEAP architecture to accurately track 6 (lateral view) and 10 (dorsal view) skeleton keypoints. The architecture achieved average precisions values of 0.90 and 0.72, average distances of 6.83 and 11.37 in pixel, and similarities of 0.94 and 0.86 for the lateral and dorsal views, respectively. These computational models are proposed as a Precision Livestock Farming tool and method for identifying and estimating postures in pigs automatically and objectively. The 2D video image repository with different pig locomotion scores can be used as a tool for teaching and research. Based on our skeleton keypoint classification results, an automatic system could be developed. This could contribute to the objective assessment of locomotion scores in sows, improving their welfare.
Assuntos
Aprendizado Profundo , Locomoção , Gravação em Vídeo , Animais , Locomoção/fisiologia , Suínos , Gravação em Vídeo/métodos , Feminino , Coxeadura Animal/diagnóstico , Coxeadura Animal/fisiopatologia , Fenômenos Biomecânicos , Doenças dos Suínos/diagnóstico , Doenças dos Suínos/fisiopatologiaRESUMO
Infrared thermography has been investigated in recent studies to monitor body surface temperature and correlate it with animal welfare and performance factors. In this context, this study proposes the use of the thermal signature method as a feature extractor from the temperature matrix obtained from regions of the body surface of laying hens (face, eye, wattle, comb, leg, and foot) to enable the construction of a computational model for heat stress level classification. In an experiment conducted in climate-controlled chambers, 192 laying hens, 34 weeks old, from two different strains (Dekalb White and Dekalb Brown) were divided into groups and housed under conditions of heat stress (35 °C and 60% humidity) and thermal comfort (26 °C and 60% humidity). Weekly, individual thermal images of the hens were collected using a thermographic camera, along with their respective rectal temperatures. Surface temperatures of the six featherless image areas of the hens' bodies were cut out. Rectal temperature was used to label each infrared thermography data as "Danger" or "Normal", and five different classifier models (Random Forest, Random Tree, Multilayer Perceptron, K-Nearest Neighbors, and Logistic Regression) for rectal temperature class were generated using the respective thermal signatures. No differences between the strains were observed in the thermal signature of surface temperature and rectal temperature. It was evidenced that the rectal temperature and the thermal signature express heat stress and comfort conditions. The Random Forest model for the face area of the laying hen achieved the highest performance (89.0%). For the wattle area, a Random Forest model also demonstrated high performance (88.3%), indicating the significance of this area in strains where it is more developed. These findings validate the method of extracting characteristics from infrared thermography. When combined with machine learning, this method has proven promising for generating classifier models of thermal stress levels in laying hen production environments.
RESUMO
The aim of this study was to develop and evaluate a machine vision algorithm to assess the pain level in horses, using an automatic computational classifier based on the Horse Grimace Scale (HGS) and trained by machine learning method. The use of the Horse Grimace Scale is dependent on a human observer, who most of the time does not have availability to evaluate the animal for long periods and must also be well trained in order to apply the evaluation system correctly. In addition, even with adequate training, the presence of an unknown person near an animal in pain can result in behavioral changes, making the evaluation more complex. As a possible solution, the automatic video-imaging system will be able to monitor pain responses in horses more accurately and in real-time, and thus allow an earlier diagnosis and more efficient treatment for the affected animals. This study is based on assessment of facial expressions of 7 horses that underwent castration, collected through a video system positioned on the top of the feeder station, capturing images at 4 distinct timepoints daily for two days before and four days after surgical castration. A labeling process was applied to build a pain facial image database and machine learning methods were used to train the computational pain classifier. The machine vision algorithm was developed through the training of a Convolutional Neural Network (CNN) that resulted in an overall accuracy of 75.8% while classifying pain on three levels: not present, moderately present, and obviously present. While classifying between two categories (pain not present and pain present) the overall accuracy reached 88.3%. Although there are some improvements to be made in order to use the system in a daily routine, the model appears promising and capable of measuring pain on images of horses automatically through facial expressions, collected from video images.
Assuntos
Reconhecimento Facial Automatizado/métodos , Orquiectomia/efeitos adversos , Medição da Dor/veterinária , Algoritmos , Animais , Bases de Dados Factuais , Aprendizado Profundo , Reconhecimento Facial , Cavalos , Humanos , Interpretação de Imagem Assistida por Computador/métodos , Redes Neurais de Computação , Orquiectomia/veterinária , Gravação em VídeoRESUMO
OBJECTIVE: This work was carried out to evaluate the effects of zilpaterol hydrochloride (ZH) and ractopamine hydrochloride (RH) combined with immunocastration on the welfare traits of feedlot Nellore cattle. METHODS: Ninety-six Nellore males (average body weight [BW] = 409±50 kg; average 20 mo of age) were divided into two groups according to BW; half of the animals in each group received two doses of an immunocastration (ImC) vaccine in a 30 day interval, and the other half did not receive the vaccine (NoC). Afterward, the animals were housed and fed a common diet for 70 days. Then, they were split into three groups and fed one of the following diets for 30 additional days: control (CO) diet, with no ß-AA; ZH diet, containing 80 mg/d ZH; and RH diet, containing 300 mg/d RH. Welfare traits were assessed by monitoring body surface temperature using infrared thermography (IRT) and plasma cortisol and temperament measurements. RESULTS: There was no interaction between sexual condition and diet for any trait. The ImC and NoC groups did not differ in rectal and ocular temperatures. The ImC animals had higher flight speeds (p = 0.022) and tended to have higher cortisol levels (p = 0.059) than the NoC animals. Animals fed ZH and RH did not differ in cortisol levels, respiratory rate, rectal temperature, temperature measured by IRT, or temperament behaviour. CONCLUSION: The ImC animals showed a less stable temperament during handling practices than NoC, whereas ZH and RH supplementation had no adverse effects on animal welfare.