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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Development of an autonomous early warning system for Bactrocera dorsalis (Hendel) outbreaks in remote fruit orchards


Developing an autonomous early warning system for detecting pest resurgence is an essential task to reduce the probabilities of massive Oriental fruit fly (Bactrocera dorsalis (Hendel)) outbreaks. By preventing pest outbreaks, farmers would be able to reduce their dependence on chemical pesticides. Chemical pesticide abuse often brings harmful consequences to human health and natural environments. Since an agroecological system can change at a fast rate due to the soil degradation and the environmental factors changes, the rise of pest density cannot be immediately detected by traditional methodologies. In this study, an autonomous early warning system, built upon the basis of wireless sensor networks and GSM networks, is presented to effectively capture long-term and up-to-the-minute natural environmental fluctuations in fruit farms. In addition, two machine learning techniques, self-organizing maps and support vector machines, are incorporated to perform adaptive learning and automatically issue a warning message to farmers and government officials via GSM networks when the population density of B. dorsalis significantly rises. The proposed system also provides sensor fault warning messages to system administrators when one or more faulty sensors give abnormal readings to the system. Then, farmers and government officials would be able to take precautionary actions in time before major pest outbreaks cause an extensive crop loss, as well as to schedule maintenance tasks to repair faulted devices. The experimental results indicate that the proposed early warning system is able to detect the incidents of possible pest outbreaks in a variety of seasonal conditions with sensitivity, specificity, accuracy, and precision around 98%, 100%, 100%, and 100%, respectively, as well as to transmit the early warning messages to farmers and government officials via Short Message Service using the GSM network. The proposed early warning system can be easily adopted in different fruit farms without extra efforts from farmers and government officials since it is built based on machine learning techniques, and the warning messages are delivered to their mobile phones as text messages. The proposed early warning system also shows great potential to assist farmers to update their pest control operations in the fruit farms, and help government officials to improve farming systems. (C) 2012 Elsevier B.V. All rights reserved.

  • TW
  • Natl_Taiwan_Univ_NTU (TW)
Data keywords
  • machine learning
Agriculture keywords
  • agriculture
  • farming
  • farm
Data topic
  • information systems
  • 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.