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
Comparison of Multiple-Layer Perceptrons and Least Squares Support Vector Machines for Remote-Sensed Characterization of In-Field LAI Patterns - A Case Study with Potato
Delineation of soil management zones in agricultural fields using reliable indicators is a major issue in precision agriculture. The leaf area index (LAI) is an important variable for the characterization of in-field variability. However, ground LAI measurement over large fields is time consuming. Our objective was to compare machine learning methods to describe in-field potato LAI patterns from airborne multispectral images. To this aim, intensive ground LAI measurements (97 quadrats) were collected in a potato field at the time of maximum LAI. Two methods were trained as function approximation, validated, and compared to linear regressions. The two methods were (i) multiple-layer perceptron (MLP) and (ii) least squares support vector machine (LS-SVM). After model training, spatial interpolation was performed and results were compared to a map interpolated with measured values. Both methods performed well using near-infrared and red channels as inputs. However, the gain in performance in validation over the best linear model was higher for the LS-SVM (29%) compared to the MLP (15%), and the kappa coefficient of agreement was higher during classification. The LS-SVM with 2 inputs (near-infrared and red) was therefore retained as the final model.
Inappropriate format for Document type, expected simple value but got array, please use list format