The Experts below are selected from a list of 960 Experts worldwide ranked by ideXlab platform
Hans Drexler - One of the best experts on this subject based on the ideXlab platform.
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Secondary prevention of allergic symptoms in a dairy farmer by use of a Milking Robot
Clinical and Molecular Allergy, 2005Co-Authors: Gintautas Korinth, Horst Christoph Broding, Wolfgang Uter, Hans DrexlerAbstract:Background Animal-derived allergens include lipocalins which play an increasing role in occupational respiratory sensitizations. The prevention of sensitization in stock farming is often difficult due to intense exposure, with traditional Milking still requiring close animal contact. Complete avoidance of allergen exposure is only possible if stock farming is abandoned. This is, however, often not feasible in small dairy plants because of the resulting loss of income. Case presentation In a 37-year-old female farmer daily asthmatic complaints appeared, associated with cow dust-derived allergen exposure by Milking with a conventional device. Respiratory symptoms increased during a period of 12 years. Allergic bronchial asthma was diagnosed, caused by sensitization against cow dust-derived allergens, as demonstrated by positive skin prick test and by detection of IgE antibodies. In a separate specific inhalation challenge test using a 10% extract of cow dust-derived allergens a 330% increase of airway resistance was detected. To enable further dairy farming, a Milking Robot was installed in 1999, i.e., an automatic Milking system. The novel Milking technique reduced the daily exposure from over 2 hours to approximately 10 min. The clinical course after the installation of the Milking Robot was favourable, with less frequent allergic and asthmatic symptoms. Furthermore, asthma medication could be reduced. Improvement was noted also in terms of lung-function and decreased total serum IgE. Conclusion The case presented and the evidence from the literature indicates that the strategy of exposure minimization to allergens at workplaces can be an effective alternative to total elimination. In farmers with cow dust allergy a Milking Robot is an appropriate technical measure to minimize allergen-exposure.
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Secondary prevention of allergic symptoms in a dairy farmer by use of a Milking Robot
Clinical and molecular allergy : CMA, 2005Co-Authors: Gintautas Korinth, Horst Christoph Broding, Wolfgang Uter, Hans DrexlerAbstract:Background Animal-derived allergens include lipocalins which play an increasing role in occupational respiratory sensitizations. The prevention of sensitization in stock farming is often difficult due to intense exposure, with traditional Milking still requiring close animal contact. Complete avoidance of allergen exposure is only possible if stock farming is abandoned. This is, however, often not feasible in small dairy plants because of the resulting loss of income.
Beom Sahng Ryuh - One of the best experts on this subject based on the ideXlab platform.
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URAI - Algorithm design for teat detection system methodology using TOF, RGBD and thermal imaging in next generation Milking Robot system
2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), 2017Co-Authors: Abhishesh Pal, Akanksha Rastogi, Song Myongseok, Beom Sahng RyuhAbstract:This research paper primarily focus on to develop a conceptual design of the vision system for an intelligent next generation Robotic Milking system for faster and accurate teat detection. In our ongoing research in laboratory environment we have been developing the intelligent vision system incorporating the technique of ToF camera imaging, RGBD imaging and thermal imaging. The teat detection system design will also be suitable for use in both new and existing Milking parlors both stall and rotary types.
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teat detection mechanism using machine learning based vision for smart automatic Milking systems
International Conference on Ubiquitous Robots and Ambient Intelligence, 2017Co-Authors: Akanksha Rastogi, Abhishesh Pal, Kim Man Joung, Beom Sahng RyuhAbstract:This paper discusses the work in progress of Automatic Milking system with focus on development of a smart vision system for the manipulator of the Milking Robot. The vision system being developed aims at giving the manipulator the capability for faster and accurate teat detection. In our laboratory environment, we are developing a smart vision system which is trained by machine learning and can detect the cow's udder and the teats for all the pre-Milking, Milking and post-Milking processes with least to none time being spent on cow herd training for the system in the barn environment. This teat detection mechanism aims to reduce the time spent on training of the system in the first few initial Milkings and reduce operator responsibilities, to eventually produce a more of plug and play product.
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algorithm design for teat detection system methodology using tof rgbd and thermal imaging in next generation Milking Robot system
International Conference on Ubiquitous Robots and Ambient Intelligence, 2017Co-Authors: Abhishesh Pal, Akanksha Rastogi, Song Myongseok, Beom Sahng RyuhAbstract:This research paper primarily focus on to develop a conceptual design of the vision system for an intelligent next generation Robotic Milking system for faster and accurate teat detection. In our ongoing research in laboratory environment we have been developing the intelligent vision system incorporating the technique of ToF camera imaging, RGBD imaging and thermal imaging. The teat detection system design will also be suitable for use in both new and existing Milking parlors both stall and rotary types.
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URAI - Teat detection mechanism using machine learning based vision for smart Automatic Milking Systems
2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), 2017Co-Authors: Akanksha Rastogi, Abhishesh Pal, Kim Man Joung, Beom Sahng RyuhAbstract:This paper discusses the work in progress of Automatic Milking system with focus on development of a smart vision system for the manipulator of the Milking Robot. The vision system being developed aims at giving the manipulator the capability for faster and accurate teat detection. In our laboratory environment, we are developing a smart vision system which is trained by machine learning and can detect the cow's udder and the teats for all the pre-Milking, Milking and post-Milking processes with least to none time being spent on cow herd training for the system in the barn environment. This teat detection mechanism aims to reduce the time spent on training of the system in the first few initial Milkings and reduce operator responsibilities, to eventually produce a more of plug and play product.
Matti Pastell - One of the best experts on this subject based on the ideXlab platform.
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Monitoring of Cow Location in a Barn by an Open-Source, Low-Cost, Low-Energy Bluetooth Tag System.
Sensors (Basel Switzerland), 2020Co-Authors: Victor Bloch, Matti PastellAbstract:Indoor localization of dairy cows is important for cow behavior recognition and effective farm management. In this paper, we propose a low-cost system for low-accuracy cow localization based on the reception of signals sent by an acceleration measurement system using the Bluetooth Low Energy protocol. The system consists of low-cost tags and receiving stations. The tag specifications and the localization accuracy of the system were studied experimentally. The received signal strength propagation model and dependence on the tag orientation was studied in an open-space and a barn environment. Two experiments for the evaluation of localization accuracy were conducted with 35 and 19 cows for two days. The localization reference was achieved from feeding stations, a Milking Robot and videos of cows decoded manually. The localization accuracy (mean ± standard deviation) was 3.27 ± 2.11 m for the entire barn (10 × 40 m2) and 1.9 ± 0.67 m for a smaller area (4 × 5 m2). The system can be used for recognizing long-distance walking, crowded areas in the barn, e.g., queues to Milking Robots, and cow's preferable locations. The estimated system cost was 500 + 20 × (cow number) € for one barn. The system has open-access software and detailed instructions for its installation and usage.
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Use of force sensors to detect and analyse lameness in dairy cows
Veterinary Record, 2008Co-Authors: Minna Kujala, Matti Pastell, Timo SoveriAbstract:Force sensors were used to detect lameness in dairy cows in two trials. In the first trial, leg weights were recorded during approximately 12,000 Milkings with balances built into the floor of the Milking Robot. Cows that put less weight on one leg or kicked frequently during Milking were checked first with a locomotion scoring system and then with a clinical inspection. A locomotion score of more than 2 was considered lame, and these cows' hooves were examined at hoof trimming to determine the cause and to identify any hoof lesions. In the second trial 315 locomotion scores were recorded and compared with force sensor data. The force sensors proved to be a good method for recognising lameness. Computer curves drawn from force sensor data helped to find differences between leg weights, thus indicating lameness and its duration. Sole ulcers and white line disease were identified more quickly by force sensors than by locomotion scoring, but joint problems were more easily detected by locomotion scoring.
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Application of CUSUM charts to detect lameness in a Milking Robot
Expert Systems with Applications, 2008Co-Authors: Matti Pastell, Henrik MadsenAbstract:In the year 2006 about 4000 farms worldwide used over 6000 Milking Robots. With increased automation the time that the cattle keeper uses for monitoring animals has decreased. This has created a need for automatic health monitoring systems. Lameness is a crucial welfare and economic issue in modern dairy husbandry. It causes problems especially in loose housing of cattle. This could be greatly reduced by early identification and treatment. A four-balance system for automatically measuring the load on each leg of a cow during Milking in a Milking Robot has been developed. It has been previously shown that the weight distribution between limbs changes when cow get lame. In this paper we suggest CUSUM charts to automatically detect lameness based on the measurements. CUSUM charts are statistical based control charts and are well suited for checking a measuring system in operation for any departure from some target or specified values. The target values for detecting lameness were calculated from the cow's own historical data so that each animal had an individual chart. The method enables objective monitoring of the changes in leg health, which is valuable information in veterinary research because it provides means for assessing the severity and impact of different causes of lameness and also evaluating the effect of treatment and medication. So far no objective method for calculating these measures has been available and the methodology presented in this paper seems very promising for the task.
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Detecting cow's lameness using force sensors
Computers and Electronics in Agriculture, 2008Co-Authors: Matti Pastell, Minna Kujala, Anna-maija Aisla, Mikko Hautala, V. Poikalainen, J. Praks, I. Veermäe, Jukka AhokasAbstract:Our aim is to automatically detect cow's leg problems. The first system is a four-balance system, where each of the legs is weighed when a cow is in a Milking Robot. Its functionality is limited to Milking Robot and complications in interpreting results occur, since often not all of the legs are properly in each balance or the cow is leaning the Robot. Therefore, we introduce a new system. It is a mat made of electromechanical film, Emfit, which can detect only dynamic forces. Its benefit is that its use is not limited to Milking Robot, but it can be set up in any corridor along which the cows walk. Preliminary tests with walking cows indicate that it has potentiality to separate lame cows from healthy cows by different force-time behaviour.
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Automatic observation of cow leg health using load sensors
Computers and Electronics in Agriculture, 2008Co-Authors: Matti Pastell, Minna Kujala, Mikko Hautala, V. Poikalainen, J. Praks, I. Veermäe, Jukka AhokasAbstract:The Milking Robot offers a unique possibility for dynamic measurements of the leg health of dairy cows. Four strain gauge scales were installed into a Milking Robot. The sensors were connected to an amplifier and the data were collected into a PC using dedicated computer programs. The measurement was automatically started and ended based on the cow ID acquired from the Milking Robot. MATLAB was used for automated data manipulation of over 10,000 data files and criteria for detecting leg injuries were analyzed. The leg weight monitoring was developed into a real-time system with software that alerts the user of possible hoof diseases and other leg problems. The system makes it possible to detect an injured leg by separately measuring the load on each leg. It is also possible to analyze the step and kick behaviour of the cow during Milking by measuring the leg weights and analyzing the number of kicks of the cow.
Gintautas Korinth - One of the best experts on this subject based on the ideXlab platform.
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Secondary prevention of allergic symptoms in a dairy farmer by use of a Milking Robot
Clinical and Molecular Allergy, 2005Co-Authors: Gintautas Korinth, Horst Christoph Broding, Wolfgang Uter, Hans DrexlerAbstract:Background Animal-derived allergens include lipocalins which play an increasing role in occupational respiratory sensitizations. The prevention of sensitization in stock farming is often difficult due to intense exposure, with traditional Milking still requiring close animal contact. Complete avoidance of allergen exposure is only possible if stock farming is abandoned. This is, however, often not feasible in small dairy plants because of the resulting loss of income. Case presentation In a 37-year-old female farmer daily asthmatic complaints appeared, associated with cow dust-derived allergen exposure by Milking with a conventional device. Respiratory symptoms increased during a period of 12 years. Allergic bronchial asthma was diagnosed, caused by sensitization against cow dust-derived allergens, as demonstrated by positive skin prick test and by detection of IgE antibodies. In a separate specific inhalation challenge test using a 10% extract of cow dust-derived allergens a 330% increase of airway resistance was detected. To enable further dairy farming, a Milking Robot was installed in 1999, i.e., an automatic Milking system. The novel Milking technique reduced the daily exposure from over 2 hours to approximately 10 min. The clinical course after the installation of the Milking Robot was favourable, with less frequent allergic and asthmatic symptoms. Furthermore, asthma medication could be reduced. Improvement was noted also in terms of lung-function and decreased total serum IgE. Conclusion The case presented and the evidence from the literature indicates that the strategy of exposure minimization to allergens at workplaces can be an effective alternative to total elimination. In farmers with cow dust allergy a Milking Robot is an appropriate technical measure to minimize allergen-exposure.
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Secondary prevention of allergic symptoms in a dairy farmer by use of a Milking Robot
Clinical and molecular allergy : CMA, 2005Co-Authors: Gintautas Korinth, Horst Christoph Broding, Wolfgang Uter, Hans DrexlerAbstract:Background Animal-derived allergens include lipocalins which play an increasing role in occupational respiratory sensitizations. The prevention of sensitization in stock farming is often difficult due to intense exposure, with traditional Milking still requiring close animal contact. Complete avoidance of allergen exposure is only possible if stock farming is abandoned. This is, however, often not feasible in small dairy plants because of the resulting loss of income.
Jukka Ahokas - One of the best experts on this subject based on the ideXlab platform.
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Detecting cow's lameness using force sensors
Computers and Electronics in Agriculture, 2008Co-Authors: Matti Pastell, Minna Kujala, Anna-maija Aisla, Mikko Hautala, V. Poikalainen, J. Praks, I. Veermäe, Jukka AhokasAbstract:Our aim is to automatically detect cow's leg problems. The first system is a four-balance system, where each of the legs is weighed when a cow is in a Milking Robot. Its functionality is limited to Milking Robot and complications in interpreting results occur, since often not all of the legs are properly in each balance or the cow is leaning the Robot. Therefore, we introduce a new system. It is a mat made of electromechanical film, Emfit, which can detect only dynamic forces. Its benefit is that its use is not limited to Milking Robot, but it can be set up in any corridor along which the cows walk. Preliminary tests with walking cows indicate that it has potentiality to separate lame cows from healthy cows by different force-time behaviour.
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Automatic observation of cow leg health using load sensors
Computers and Electronics in Agriculture, 2008Co-Authors: Matti Pastell, Minna Kujala, Mikko Hautala, V. Poikalainen, J. Praks, I. Veermäe, Jukka AhokasAbstract:The Milking Robot offers a unique possibility for dynamic measurements of the leg health of dairy cows. Four strain gauge scales were installed into a Milking Robot. The sensors were connected to an amplifier and the data were collected into a PC using dedicated computer programs. The measurement was automatically started and ended based on the cow ID acquired from the Milking Robot. MATLAB was used for automated data manipulation of over 10,000 data files and criteria for detecting leg injuries were analyzed. The leg weight monitoring was developed into a real-time system with software that alerts the user of possible hoof diseases and other leg problems. The system makes it possible to detect an injured leg by separately measuring the load on each leg. It is also possible to analyze the step and kick behaviour of the cow during Milking by measuring the leg weights and analyzing the number of kicks of the cow.
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Contactless measurement of cow behavior in a Milking Robot
Behavior Research Methods, 2006Co-Authors: M. Pastell, Anna-maija Aisla, Mikko Hautala, V. Poikalainen, J. Praks, I. Veermäe, Jukka AhokasAbstract:We have worked on automatically measuring the behavior of dairy cows during automatic Milking. A Milking Robot offers a unique possibility for a dynamic measurement of physical data. Four strain gauge scales were installed into a Milking Robot in order to measure the weight of each leg separately, and a laser distance sensor was placed next to the Robot in order to measure the radial movement of the cow’s body surface. The data were collected into a PC. Three video cameras were installed to observe the system, and the data were recorded digitally. From the data, the dynamic weight or load of each leg and the respiration rate of a cow could be measured. Different stages of Milking were observed, and the changes in behavior during Milking were analyzed. The acquired information could be used to judge a cow’s restlessness and welfare—for example, leg health and stress.
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Automatic Cow Health Measurement System in a Milking Robot
2006 Portland Oregon July 9-12 2006, 2006Co-Authors: Matti Pastell, Anna-maija Aisla, Mikko Hautala, Jukka Ahokas, V. Poikalainen, J. Praks, I. VeermäeAbstract:Milking Robot offers a unique possibility for dynamic measurements of physiological data. We have worked on automatically measuring health of dairy cows during automatic Milking. The measurement system consists of leg health measurement system and a respiration rate measurement. Four strain gauge scales were installed into a Milking Robot in order to measure the weight of each leg separately and a laser distance sensor was placed next to the Robot in order to measure the radial movement of cow’s body surface. The sensors were connected to an amplifier and the data was collected into a PC using a dedicated computer program. Three video cameras were installed to observe the system and the data was recorded digitally. From the data the dynamic weight or load of each leg and the respiration rate of the cow can be measured. The average weight, the weight variation of each leg, the total weight, the number of kicks and steps, the frequency of kicks and the total time in the Milking Robot can be calculated. The data can be used for automatic detection of leg problems and it is highly probable that the symptoms of heat stress can be detected as increased respiration rate.
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Assessing cows' welfare : weighing the cow in a Milking Robot
Biosystems Engineering, 2006Co-Authors: Matti Pastell, Minna Kujala, Mikko Hautala, V. Poikalainen, J. Praks, I. Veermäe, H. Takko, H. Gröhn, Jukka AhokasAbstract:Four strain gauge balances were installed into a Milking Robot after careful inspection of the positions of the legs of all 40 cows from a herd. It was found that 90% of the cows would have all the legs on the balances at least during every second Milking. The balances were connected to a four channel amplifier and the data were targeted to a personal computer using a dedicated computer program. From the data, the dynamic weight or load of each leg can be measured. The average weight, the weight variation of each leg, the total weight, the number of kicks, the frequency of kicks and the total time in the Milking Robot were calculated. The changes in values of each cow were followed and the leg health of cows was observed. Preliminary analysis of the data gives evidence that limb and hoof disorders can be detected with the system. It is also possible to analyse the step and kick behaviour of the cow during Milking and during the different stages of Milking, washing, Milking and disconnecting. In this way it is also possible to monitor the activity level of the cow and how it changes.