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.

You can access and play with the graphs:

Discover all records
Home page


Application of fuzzy cognitive maps in precision agriculture: a case study on coconut yield management of southern India's Malabar region


Coconut is one of the major perennial food crops that has a long development phase of 44 months. The climatic and seasonal variations affect all stages of coconut's long development cycle. Besides, the soil composition also plays a vital role in deciding the coconut yield behavior. The present study is focused on categorizing the coconut production level for the given set of agro-climatic conditions using the methodology of fuzzy cognitive map (FCM) enhanced by its learning capabilities. Additionally, an attempt is made to study the impact of climatic variations and weather parameters on the coconut yield behavior using the reasoning capabilities of FCM. Real coconut field data of different seasons for the period from 2009 to 2013 of Kerala state's Malabar region were used for training and evaluation of the FCM. The present work demonstrates the classification and prediction capabilities of FCM for the described precision agriculture application, with the two most known and efficient FCM learning approaches, viz., nonlinear Hebbian (NHL) and data-driven nonlinear Hebbian (DDNHL). The DDNHL-FCM offers an overall classification accuracy of 96 %. The various case studies furnished in the paper demonstrate the power of NHL-FCM in effectively reasoning new knowledge pertaining to the presented precision agriculture application.

  • IN
  • GR
  • Anna_Univ (IN)
  • Technol_Educ_Inst_Lamias (GR)
  • Minist_Educ_Res_&_Relig_Affairs (GR)
Data keywords
  • machine learning
  • knowledge
  • reasoning
Agriculture keywords
  • agriculture
Data topic
  • big data
  • information systems
  • modeling
  • sensors
Document type

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

Institutions 10 co-publis
    Powered by Lodex 8.20.3
    logo commission europeenne
    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.