The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
Present study describes the application of machine learning algorithms to detect the health status of Karon Fries crossbred cow, whether cow is lame or healthy. A total of 589 Karan Fries crossbred cows data from the herd maintained at National Dairy Research Institute (NDRI), Karnal were recorded for the health status (lame or healthy) as target variable and other non-genetic variables such as percent of body weight distributed to individual legs (using load cell platform), parity (11010), status of pregnancy (non-pregnant, less than 90days, 91-180days and more than 181days pregnant), status of lactation (less than 60days, 61-120days, more than 121days and dry), and daily milk yield were used as input variables. These variables were used for the development of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) neural networks (NNs). Simulation of network were carried out using varying data partition strategies for training and validation data sets (60:40, 70:30 and 80:20), number of nodes in hidden layers and different optimization algorithms. It was found that to predict the heath status of cows RBF neural architect with Leven-burg Marquardt (LM) optimization algorithm, gave the best performance in comparison to MLP with highest classification accuracy rate (83.19%) at 80:20 data partition strategy.
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