Mastitis is a complex, multifactorial disease. Pathogens, cows and farmers （via management） all play a role. It is costly and annoying for the farmer and threatens the image of the entire dairy industry. Prevention ...Mastitis is a complex, multifactorial disease. Pathogens, cows and farmers （via management） all play a role. It is costly and annoying for the farmer and threatens the image of the entire dairy industry. Prevention and control of mastitis is based on multiple principles that have been known for a long time. To implement them successfully, they should be put forward by a motivated and motivating advisor that transfers the existing knowledge to the farmer. When the changes are data-driven, applied by an encouraged farmer through a farm-specific implementation, prevention and control of mastitis will be successful and result in happy cows, happy farmers, happy advisors, happy consumers, and a happy industry. Nationwide projects focussing on communication and transfer of existing knowledge in prevention and control are very helpful in reaching high numbers of farmers and advisors and harmonizing the message brought by different parties. This paper gives an overview of multifactorial approach of mastitis management and prevention with a focus on milking, bedding and data-analysis.展开更多
Computer vision is a consistent and advanced technique for image processing,with the propitious outcome,and enormous potential.A computer vision has been strongly adopted in the heterogeneous domain including agricult...Computer vision is a consistent and advanced technique for image processing,with the propitious outcome,and enormous potential.A computer vision has been strongly adopted in the heterogeneous domain including agriculture.During the study of existing research on the role of computer vision in fruits and vegetables among various horticulture products of agriculture fields it is noticed that,the existing survey paper has not focused properly on mathematical framework,feature descriptor,defect detection on multiple datasets of fruits and vegetables elaborately.This has motivated us to undertake an extensive survey.In this paper,we examine the paper broadly related to fruits and vegetables among various horticulture products of agriculture fields,specific model,data pre-processing,data analysis method and overall value of performance accuracy by using a particular performance metric.Moreover,we study the different type of disease present in various fruit and vegetable.We have also focused on the comparison of different machine learning approach with respect to different performance metrics on the same dataset.Thus,we have found that among all existing machine learning techniques SVM give better classification accuracy.A generalized framework to grade the quality and defect detection of multiple fruits and vegetables is also proposed in this survey.This paper covers the survey of ninety-eight papers closely related to computer vision in the agricultural field.By the survey,we have found that computer vision plays an important role and has a large potential to address the challenges related to the agricultural fields.展开更多
Modern interactive tools for data analysis and visualisation are designed to expose their functionalities as a service through the Web.We present in this paper a Web API(SWIRRL)that allows Virtual Research Environment...Modern interactive tools for data analysis and visualisation are designed to expose their functionalities as a service through the Web.We present in this paper a Web API(SWIRRL)that allows Virtual Research Environments(VREs)to easily integrate such tools in their websites and re-purpose them to their users.The APl deals,on behalf of the clients,with the underlying complexity of allocating and managing resources within a target cloud platform.By combining storage and containerised services,offering analysis notebooks and other visualisation software,the APl creates dedicated working sessions on-demand,which can be accessed collaboratively.Thanks to the API's support for workflow execution,SWIRRL workspaces can be automatically populated with data of interest collected from external data providers.The system keeps track of updates and changes affecting the data and the tools by adopting versioning and standard provenance technologies.Users are provided with interactive controls enabling traceabilityand recovery actions,including the possibility of creating executable snapshots of their environments.SWIRRL is built in cooperation with two research infrastructures in the field of solid earth science and climate data modeling.We report on the particularadoptions and use cases.展开更多
文摘Mastitis is a complex, multifactorial disease. Pathogens, cows and farmers （via management） all play a role. It is costly and annoying for the farmer and threatens the image of the entire dairy industry. Prevention and control of mastitis is based on multiple principles that have been known for a long time. To implement them successfully, they should be put forward by a motivated and motivating advisor that transfers the existing knowledge to the farmer. When the changes are data-driven, applied by an encouraged farmer through a farm-specific implementation, prevention and control of mastitis will be successful and result in happy cows, happy farmers, happy advisors, happy consumers, and a happy industry. Nationwide projects focussing on communication and transfer of existing knowledge in prevention and control are very helpful in reaching high numbers of farmers and advisors and harmonizing the message brought by different parties. This paper gives an overview of multifactorial approach of mastitis management and prevention with a focus on milking, bedding and data-analysis.
文摘Computer vision is a consistent and advanced technique for image processing,with the propitious outcome,and enormous potential.A computer vision has been strongly adopted in the heterogeneous domain including agriculture.During the study of existing research on the role of computer vision in fruits and vegetables among various horticulture products of agriculture fields it is noticed that,the existing survey paper has not focused properly on mathematical framework,feature descriptor,defect detection on multiple datasets of fruits and vegetables elaborately.This has motivated us to undertake an extensive survey.In this paper,we examine the paper broadly related to fruits and vegetables among various horticulture products of agriculture fields,specific model,data pre-processing,data analysis method and overall value of performance accuracy by using a particular performance metric.Moreover,we study the different type of disease present in various fruit and vegetable.We have also focused on the comparison of different machine learning approach with respect to different performance metrics on the same dataset.Thus,we have found that among all existing machine learning techniques SVM give better classification accuracy.A generalized framework to grade the quality and defect detection of multiple fruits and vegetables is also proposed in this survey.This paper covers the survey of ninety-eight papers closely related to computer vision in the agricultural field.By the survey,we have found that computer vision plays an important role and has a large potential to address the challenges related to the agricultural fields.
基金supported by the EU H2020 project ENVRIFair(No.824068)ISENES3(No.824084).
文摘Modern interactive tools for data analysis and visualisation are designed to expose their functionalities as a service through the Web.We present in this paper a Web API(SWIRRL)that allows Virtual Research Environments(VREs)to easily integrate such tools in their websites and re-purpose them to their users.The APl deals,on behalf of the clients,with the underlying complexity of allocating and managing resources within a target cloud platform.By combining storage and containerised services,offering analysis notebooks and other visualisation software,the APl creates dedicated working sessions on-demand,which can be accessed collaboratively.Thanks to the API's support for workflow execution,SWIRRL workspaces can be automatically populated with data of interest collected from external data providers.The system keeps track of updates and changes affecting the data and the tools by adopting versioning and standard provenance technologies.Users are provided with interactive controls enabling traceabilityand recovery actions,including the possibility of creating executable snapshots of their environments.SWIRRL is built in cooperation with two research infrastructures in the field of solid earth science and climate data modeling.We report on the particularadoptions and use cases.
