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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Optimizing Neural Network Classification by Using the Cuckoo Algorithm


Classification has become a very important field in the current era of big data. As one of the main stream algorithms for classification, the well-known Error Back Propagation algorithm, with the characteristic of nonlinear mapping, good self-study ability and fault tolerance ability, has been pervasively applied in finance, agriculture, industry and other fields. However, the Error Back Propagation algorithm would face the problems of low accuracy, poor stability and slow convergence if the weights and thresholds are set improperly. In this paper, the Cuckoo algorithm is employed to train the weights and thresholds of the Error Back Propagation algorithm. From the aspects of accuracy, stability and time cost, experiments and performance comparisons towards the basic Error Back Propagation algorithm model (BP), the improved neural network model based on Cuckoo algorithm (BPCS) and the improved neural network model based on Genetic algorithm (GABP) are organized by using two classification datasets, respectively. The results show that the neural network optimizing by Cuckoo algorithm has faster convergence speed, higher accuracy and better stability than others. In addition, the ranges for selecting parameters are suggested based on an appropriate model.

  • CN
    Data keywords
    • big data
    Agriculture keywords
    • agriculture
    Data topic
    • big data
    • modeling
    • sensors
    Document type

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

    Institutions 10 co-publis
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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.