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
Exploring Low Cost Laser Sensors to Identify Flying Insect Species Evaluation of Machine Learning and Signal Processing Methods
Insects have a close relationship with the humanity, in both positive and negative ways. Mosquito borne diseases kill millions of people and insect pests consume and destroy around US $40 billion worth of food each year. In contrast, insects pollinate at least two-thirds of all the food consumed in the world. In order to control populations of disease vectors and agricultural pests, researchers in entomology have developed numerous methods including chemical, biological and mechanical approaches. However, without the knowledge of the exact location of the insects, the use of these techniques becomes costly and inefficient. We are developing a novel sensor as a tool to control disease vectors and agricultural pests. This sensor, which is built from inexpensive commodity electronics, captures insect flight information using laser light and classifies the insects according to their species. The use of machine learning techniques allows the sensor to automatically identify the species without human intervention. Finally, the sensor can provide real-time estimates of insect species with virtually no time gap between the insect identification and the delivery of population estimates. In this paper, we present our solution to the most important challenge to make this sensor practical: the creation of an accurate classification system. We show that, with the correct combination of feature extraction and machine learning techniques, we can achieve an accuracy of almost 90 % in the task of identifying the correct insect species among nine species. Specifically, we show that we can achieve an accuracy of 95 % in the task of correctly recognizing if a given event was generated by a disease vector mosquito.
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