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
Greenhouse environmental control systems using sensor networks are becoming more widespread and sophisticated. To match the produce of expert farmers, these systems collect data about cultivation environment and growth situation, and aim to control the environment for cultivating high quality crops. However, with no agriculture experience, it is difficult for system users to set control parameters of several devices properly. In order to reproduce prediction control performed by expert farmers' cultivation without human intervention, the authors propose a smart greenhouse environmental control system based on sliding window-based support vector regression (SW-SVR). The proposed system performs prediction control based on accurate predictions in real time. SW-SVR is a new machine learning algorithm for time series data prediction. The prediction model automatically adjusts to the current environment periodically, predicts time series data with high accuracy and low computational complexity. The proposed system using SW-SVR enables system users to optimize controls for crops. Meanwhile, since plant growth is related to the photosynthesis and transpiration of leaves, the authors developed wireless scattered light sensors which measure leaf area size indirectly so as to estimate plant growth. Our experimental results, using data of scattered light sensors on-site, outside weather data, and forecast data as independent variables of SW-SVR for hydroponic culture of tomatoes, show the proposed system reduced prediction error of nitrogen absorption amount by 59.44% as Mean Absolute Error (MAE) and 52.89% as Root Mean Squared Error (RMSE) compared with SVR, and reduced training data by 43.07% on average. Furthermore, the sugar content of tomatoes cultivated by the prototype system increased 1.54 times compared with usual tomatoes. (C) 2015 The Authors. Published by Elsevier B.V.
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