e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

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.

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An Online Software for Decision Tree Classification and Visualization using C4.5 Algorithm (ODTC)


Classification is an important and widely carried out task of data mining. It is a predictive modelling task which is defined as building a model for the target variable as a function of the explanatory variables. There are many well established techniques for classification, while decision tree is a very important and popular technique from the machine learning domain. Decision tree is a decision_support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs and utility. C4.5 is a well known decision tree algorithm used for classifying datasets. The C4.5 algorithm is Quinlan's extension of his own ID3 algorithm for decision tree classification. It induces decision trees and generates rules from datasets, which could contain categorical and/or numerical attributes. The rules could he used to predict categorical values of attributes from new records. C4.5 performs well in classifying the dataset as well as in generating useful rules. In this paper, a web based software for rule generation and decision tree induction using C4.5 algorithm has been discussed. The visualization in the form of tree structure enhances the understanding of the generated rules. The software contains the feature to impute the missing values in data. The input data can both he categorical and numerical in nature. The software can import TXT, XLS and CSV data file formats. Enhanced waterfall model has been used for the software development process. This software will he useful for academicians, researchers and students working in the area of data mining, agriculture and other fields where huge amount of data is generated.

  • IN
  • ICAR_Indian_Council_Agr_Res (IN)
Data keywords
  • machine learning
Agriculture keywords
  • agriculture
Data topic
  • big data
  • information systems
  • modeling
Document type

Inappropriate format for Document type, expected simple value but got array, please use list format

Institutions 10 co-publis
  • ICAR_Indian_Council_Agr_Res (IN)
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e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.