Lameness detection is a world-wide challenge to far-mers and veterinarians. Traditionally, one uses visual observation to make judgment on a cow's lameness or soundness. Visual observation heavily depends on the o...Lameness detection is a world-wide challenge to far-mers and veterinarians. Traditionally, one uses visual observation to make judgment on a cow's lameness or soundness. Visual observation heavily depends on the observer's experience, hence is subjective or ob-server-dependent. And even worse, it is inconsistent. It's reported that the agreement between veterinarians can be as low as 45% [1]. It is necessary and urgent to develop an objective detection method that can automatically detect lameness when it occurs. In this paper, we describe how statistical models can be used to develop such methods and how well the statistical models perform.展开更多
This study proposed a method for detecting lameness in dairy cows based on machine vision,addressing the challenges associated with manual detection.Data from a dairy farm in Taigu,Shanxi,China were collected and divi...This study proposed a method for detecting lameness in dairy cows based on machine vision,addressing the challenges associated with manual detection.Data from a dairy farm in Taigu,Shanxi,China were collected and divided into two parts.The first part was utilized to precisely position the cow’s back by employing a dedicated deep learning model named GhostNet_YOLOv4,which can be implemented on mobile or embedded devices.The second part was used with the Visual Background Extractor(Vibe)algorithm,incorporating additional morphological processing techniques.Enhancing the Vibe algorithm,a widely used background subtraction algorithm for image sequences,achieved more accurate recognition of the specific pixel areas of cows.Subsequently,cow shape-related feature parameters were extracted from the back area using the combined approach.These parameters were used to calculate the average curvature,which describes the degree of curvature of the cow’s back contour during walking.The differences in curvature values were employed for classification to detect lameness.Through extensive experimentation,distinct average curvature ranges of[−0.025,−0.125],[−0.025,+∞],and[−∞,−0.125]were established for normal cows,early lameness,and moderate-severe lameness,respectively.The algorithm’s effectiveness was validated by processing 600 image sequences of dairy cows,resulting in a lameness detection accuracy of 91.67%.These findings can serve as a reference for the timely and accurate recognition of lameness in dairy cows.展开更多
Bacterial infections are quite common in dairy cattle,and frequently related to internal organ systems like e.g.respiratory,intestinal and udder infections.Lameness in dairy cattle is mainly caused by both infectious ...Bacterial infections are quite common in dairy cattle,and frequently related to internal organ systems like e.g.respiratory,intestinal and udder infections.Lameness in dairy cattle is mainly caused by both infectious and non-infectious hoof problems and these have different etiological background.At the moment(2018)the major infectious hoof disorders are DD(Digital Dermatitis)and IP(Interdigital Phlegmon).These are all due to infection from the area where dairy cows normally live and more or less intensive contact with“contaminated”manure.This paper gives insight in these different dermatitis problems cows are daily confronted with,with a focus on infectious hoof disorders as a more or less permanent problem in today’s dairy farming.展开更多
Compost barns for dairy cows are showing increased popularity also in Central Europe. A compost barn is used mainly as a two-area system with a bedded lying area and a solid feeding alley. Sawdust or dry fine wood sha...Compost barns for dairy cows are showing increased popularity also in Central Europe. A compost barn is used mainly as a two-area system with a bedded lying area and a solid feeding alley. Sawdust or dry fine wood shavings or wood chips are mostly used as bedding material, which has to be stirred twice a day. Stirring aerates and mixes faeces and urine into the bedding material, the mixture decomposes by means of aerobic microorganisms. A joint research project between the Agricultural Research and Education Centre Raumberg-Gumpenstein (HBLFA) and the Institute for Sustainability Sciences Tänikon (ISS) analyzed amongst other things, the cleanliness of the animals, integument alterations, lying behaviour and the current lameness situation of animals. A total of 138 cows were examined on five Austrian dairy farms. All cows were visually scored and animal behaviour was observed by data loggers as well as by direct observation. The mean value concerning cleanliness of animals was 0.44, while the udder was the cleanest and the lower leg the dirtiest area. Only a few lesions in carpal and tarsal joints could be found. Cows showed no differences in lying behaviour between times of day and temperatures. Large differences in lying behaviour were evident among farms. While on the compost barn farms only around 25% of all cows were scored to be lame, on cubicle-housing system farms 31% - 46% of the cows fell into that category (p < 0.001). From the present results, the compost barn can be seen as an animal-friendly system. In further investigations other factors affecting animal health and to resolve any outstanding issues concerning economy and alternative litter materials should be analyzed.展开更多
The purpose of this study was to evaluate the effect of intralesional Mesenchymal Stem Cells (MSC) on the treatment of experimentally induced articular chondral defects in horses, emphasizing the benefits of this appl...The purpose of this study was to evaluate the effect of intralesional Mesenchymal Stem Cells (MSC) on the treatment of experimentally induced articular chondral defects in horses, emphasizing the benefits of this application in veterinary medicine. Chondral defects were induced in the medial femoral trochlea of both hind limbs of four horses. Thirty days post induction;the horses were divided into two groups. The G1 was submitted to treatment with MSC and the G2 was the control group. Clinical evaluations, synovial fluid analysis and synovial Prostaglandin E2 (PGE2) assessment were performed prior to defects and fortnightly up to 120 and 150 days. Macroscopic, histopathological and histochemical evaluations were performed at the end of the experiment. The treatment with MSC reduced the intraarticular inflammatory process. The G1 showed lower PGE2 concentrations in the synovial fluid and greater percentage of mononuclear cells and lower percentages of lymphocytes and neutrophils. The treatment improved the macro and microscopic aspects of repair tissue. No difference was observed in the scores of lameness between the G1 and G2. The use of MSC in the treatment of chondral defects minimized joint inflammation, as confirmed by synovial fluid analysis. The treatment resulted in an improved repair tissue, verified by macroscopic examination, histochemical and histopathological analysis.展开更多
文摘Lameness detection is a world-wide challenge to far-mers and veterinarians. Traditionally, one uses visual observation to make judgment on a cow's lameness or soundness. Visual observation heavily depends on the observer's experience, hence is subjective or ob-server-dependent. And even worse, it is inconsistent. It's reported that the agreement between veterinarians can be as low as 45% [1]. It is necessary and urgent to develop an objective detection method that can automatically detect lameness when it occurs. In this paper, we describe how statistical models can be used to develop such methods and how well the statistical models perform.
基金This work was supported by Shanxi Province Basic Research Program(Free Exploration)Project(No:202103021224149)Shanxi Province Postgraduate Education Teaching Reform Project(2021YJJG087)Shanxi Province Educational Science“14th Five-Year Plan”Education Evaluation Special Project(PJ-21001)funded.
文摘This study proposed a method for detecting lameness in dairy cows based on machine vision,addressing the challenges associated with manual detection.Data from a dairy farm in Taigu,Shanxi,China were collected and divided into two parts.The first part was utilized to precisely position the cow’s back by employing a dedicated deep learning model named GhostNet_YOLOv4,which can be implemented on mobile or embedded devices.The second part was used with the Visual Background Extractor(Vibe)algorithm,incorporating additional morphological processing techniques.Enhancing the Vibe algorithm,a widely used background subtraction algorithm for image sequences,achieved more accurate recognition of the specific pixel areas of cows.Subsequently,cow shape-related feature parameters were extracted from the back area using the combined approach.These parameters were used to calculate the average curvature,which describes the degree of curvature of the cow’s back contour during walking.The differences in curvature values were employed for classification to detect lameness.Through extensive experimentation,distinct average curvature ranges of[−0.025,−0.125],[−0.025,+∞],and[−∞,−0.125]were established for normal cows,early lameness,and moderate-severe lameness,respectively.The algorithm’s effectiveness was validated by processing 600 image sequences of dairy cows,resulting in a lameness detection accuracy of 91.67%.These findings can serve as a reference for the timely and accurate recognition of lameness in dairy cows.
文摘Bacterial infections are quite common in dairy cattle,and frequently related to internal organ systems like e.g.respiratory,intestinal and udder infections.Lameness in dairy cattle is mainly caused by both infectious and non-infectious hoof problems and these have different etiological background.At the moment(2018)the major infectious hoof disorders are DD(Digital Dermatitis)and IP(Interdigital Phlegmon).These are all due to infection from the area where dairy cows normally live and more or less intensive contact with“contaminated”manure.This paper gives insight in these different dermatitis problems cows are daily confronted with,with a focus on infectious hoof disorders as a more or less permanent problem in today’s dairy farming.
文摘Compost barns for dairy cows are showing increased popularity also in Central Europe. A compost barn is used mainly as a two-area system with a bedded lying area and a solid feeding alley. Sawdust or dry fine wood shavings or wood chips are mostly used as bedding material, which has to be stirred twice a day. Stirring aerates and mixes faeces and urine into the bedding material, the mixture decomposes by means of aerobic microorganisms. A joint research project between the Agricultural Research and Education Centre Raumberg-Gumpenstein (HBLFA) and the Institute for Sustainability Sciences Tänikon (ISS) analyzed amongst other things, the cleanliness of the animals, integument alterations, lying behaviour and the current lameness situation of animals. A total of 138 cows were examined on five Austrian dairy farms. All cows were visually scored and animal behaviour was observed by data loggers as well as by direct observation. The mean value concerning cleanliness of animals was 0.44, while the udder was the cleanest and the lower leg the dirtiest area. Only a few lesions in carpal and tarsal joints could be found. Cows showed no differences in lying behaviour between times of day and temperatures. Large differences in lying behaviour were evident among farms. While on the compost barn farms only around 25% of all cows were scored to be lame, on cubicle-housing system farms 31% - 46% of the cows fell into that category (p < 0.001). From the present results, the compost barn can be seen as an animal-friendly system. In further investigations other factors affecting animal health and to resolve any outstanding issues concerning economy and alternative litter materials should be analyzed.
基金financially supported by the FAPESP(Sao Paulo Research Foundation)(2008/56360-7 and 2009/06059-1).
文摘The purpose of this study was to evaluate the effect of intralesional Mesenchymal Stem Cells (MSC) on the treatment of experimentally induced articular chondral defects in horses, emphasizing the benefits of this application in veterinary medicine. Chondral defects were induced in the medial femoral trochlea of both hind limbs of four horses. Thirty days post induction;the horses were divided into two groups. The G1 was submitted to treatment with MSC and the G2 was the control group. Clinical evaluations, synovial fluid analysis and synovial Prostaglandin E2 (PGE2) assessment were performed prior to defects and fortnightly up to 120 and 150 days. Macroscopic, histopathological and histochemical evaluations were performed at the end of the experiment. The treatment with MSC reduced the intraarticular inflammatory process. The G1 showed lower PGE2 concentrations in the synovial fluid and greater percentage of mononuclear cells and lower percentages of lymphocytes and neutrophils. The treatment improved the macro and microscopic aspects of repair tissue. No difference was observed in the scores of lameness between the G1 and G2. The use of MSC in the treatment of chondral defects minimized joint inflammation, as confirmed by synovial fluid analysis. The treatment resulted in an improved repair tissue, verified by macroscopic examination, histochemical and histopathological analysis.
