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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Joint Crop and Tassel Segmentation in the Wild


Crop segmentation is a frequently concerned problem for computer vision applications in agriculture. Tassel is a typical agronomic trait in the crop breeding process. Tassel trait characterization also requires fine-grained shape extraction. However, previous methods are usually dependent of category, which is hard to transfer to other cultivars with different colors. To address this, the goal of this study is to develop a feasible method that can deal with different categories simultaneously and that is easy to transfer. Targeted on maize, we proposed to jointly segment crop and maize tassel. The task is consequently formulated as a semantic segmentation problem. We proposed a region-based approach that leverages the efficient graph-based segmentation algorithm and simple linear iterative clustering (SLIC) to generate region proposals. Then, a neural network based color model is learnt to execute the semantic labeling. We demonstrate the effectiveness of our method on two typical crop and tassel dataset respectively. Experimental Results show that our approach significantly outperforms other state-of-the-art approaches on the tassel segmentation and achieves comparable performance on the traditional crop segmentation. Results of this research can serve to the agriculture automation, mechanization and intellectualization. The dataset and source code are made available online.

  • CN
  • Huazhong_Univ_Sci_&_Technol_HUST (CN)
Data keywords
  • semantic
  • graph based
Agriculture keywords
  • agriculture
Data topic
  • semantics
  • sensors
2015 Chinese Automation Congress (CAC)
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.