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:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
Contextual Soft Classification Approaches for Crops Identification Using Multi-sensory Remote Sensing Data: Machine Learning Perspective for Satellite Images
Agriculture department plays vital role on forecasting crop production and acreage estimation in the State. Commodity market estimates crop production on the basis of crop mass arrival in market and field prediction from authorized sources like Crop Advisory Boards. However, it is obvious that estimates from such board and government are often remains unmatched due to non-qualitative and unreliable approaches. The timely and accurate acreage estimation of crop is the pre-requisite for the purpose of better management upon crop production estimation. The conventional methods of gathering information on crop acreage are cumbersome, costly, and protracted, especially when the extent of work is whole county. The crop acreage statistics proves more crucial in event of natural calamity for taking strategic decisions like compensations to farmers based on losses they come up with. In a nutshell, non-availibity of accurate and finely estimated forecast necessitates the formation of coherent policy on fixing up agricultural commodity prices. Finally, soft classification approaches proved to be an alternative to error prone crop statistics by virtue of machine learning algorithms that applied on remote sensing images, a third eye technology which never lies. This paper conferred about gamut of machine learning algorithms for satellite data applications and envisages future trends that would be a magnet for researchers in upcoming years.
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