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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Fuzzy cognitive map based approach for predicting yield in cotton crop production as a basis for decision_support system in precision agriculture application


This work investigates the process of yield prediction in cotton crop production using the soft computing technique of fuzzy cognitive maps. Fuzzy cognitive map (FCM) is a fusion of fuzzy logic and cognitive map theories, and is used for modeling and representing experts' knowledge. It is capable of dealing with situations including uncertain descriptions using similar procedure such as human reasoning does. It is a challenging approach for decision making especially in complex processing environments. The FCM approach presented here was chosen to be utilized in agriculture because of the nature of the application. The prediction of yield in cotton production is a complex process with sufficient interacting parameters and FCMs are suitable for this kind of problem. Throughout this proposed method, FCMs designed and developed to represent experts' knowledge for cotton (Gossypium hirsutum L.) yield prediction and crop management. The developed FCM model consists of nodes linked by directed edges, where the nodes represent the main factors affecting cotton crop production such as texture, organic matter, pH, K, P, Mg, N, Ca, Na and cotton yield, and the directed edges show the cause-effect (weighted) relationships between the soil properties and cotton yield. The investigated methodology was evaluated for 360 cases measured during the time of six subsequent years (2001-2006) in a 5 ha experimental cotton field, in predicting the yield class between two possible categories ("low" and "high"). The results obtained reveal its comparative advantage over the benchmarking machine learning algorithms tested for the same data set for the years mentioned by providing decisions that match better with the real measured ones. The main advantage of this approach is its simple structure and flexibility, representing knowledge visually and more descriptively. Hence, it might be a convenient tool in predicting cotton yield and improving crop management. (C) 2011 Elsevier B.V. All rights reserved.

  • GR
  • Univ_Thessaly (GR)
  • Technol_Educ_Inst_Lamias (GR)
Data keywords
  • knowledge
  • knowledge representation
  • machine learning
  • reasoning
Agriculture keywords
  • agriculture
Data topic
  • information systems
  • modeling
  • decision support
  • semantics
  • sensors
Document type

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

Institutions 10 co-publis
  • Univ_Thessaly (GR)
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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.