Special Issue "State-of-the-Art Sensors Technology in Spain 2013"
Deadline for manuscript submissions: 30 June 2013
Prof. Dr. Gonzalo Pajares Martinsanz
Dpt. Software Engineering and Artificial Intelligence, Faculty of Informatics, University Complutense of Madrid, 28040 Madrid, Spain
Interests: computer vision; image processing; pattern recognition; 3D image reconstruction, spatio-temporal image change detection and track movement; fusion and registering from imaging sensors; superresolution from low-resolution image sensors
The aim of this special issue is to provide a comprehensive view on the state-of-the-art sensors technology in Spain. Research articles are solicited which will provide a consolidated state-of-the-art in this area. The Special Issue will publish those full research, review and high rated manuscripts addressing the above topic.
Prof. Dr. Gonzalo Pajares Martinsanz
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed Open Access monthly journal published by MDPI.
- chemical sensors
- physical sensors
Article: Identifying the Key Factors Affecting Warning Message Dissemination in VANET Real Urban Scenarios
Sensors 2013, 13(4), 5220-5250; doi:10.3390/s130405220
Received: 21 February 2013; in revised form: 8 April 2013 / Accepted: 12 April 2013 / Published: 19 April 2013| Download PDF Full-text (627 KB) | Download XML Full-text
Sensors 2013, 13(4), 5381-5402; doi:10.3390/s130405381
Received: 21 February 2013; in revised form: 12 April 2013 / Accepted: 12 April 2013 / Published: 22 April 2013| Download PDF Full-text (1323 KB) | Download XML Full-text
Sensors 2013, 13(6), 7786-7796; doi:10.3390/s130607786
Received: 6 April 2013; in revised form: 4 June 2013 / Accepted: 6 June 2013 / Published: 18 June 2013| Download PDF Full-text (300 KB)
Article: Combination of RGB and Multispectral Imagery for Discrimination of Cabernet Sauvignon Grapevine Elements
Sensors 2013, 13(6), 7838-7859; doi:10.3390/s130607838
Received: 6 May 2013; in revised form: 13 June 2013 / Accepted: 17 June 2013 / Published: 19 June 2013| Download PDF Full-text (2217 KB)
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Plant Biometrics: The Key to Prediction
Authors: Francisco Rovira-Más and Verónica Sáiz-Rubio
Affiliation: Agricultural Robotics Laboratory, Polytechnic University of Valencia, Spain
Abstract: The sustainability of agricultural production in the twenty-first century, both in industrialized and developing countries, benefits from the integration of farm management with information technology (IT) such that individual plants, rows, or subfields may be endowed with a singular “identity.” This approach approximates the nature of agricultural processes to the engineering of industrial processes. In order to cope with the vast variability of nature and the uncertainties of working outdoors, the concept of plant biometrics is defined as the scientific analysis of agricultural observations confined to spaces of reduced dimensions and known position with the purpose of building prediction models. This article develops the idea of plant biometrics by setting its principles, explaining the selection of plant biometric traits, discussing the way to quantize traits, and analyzing the mathematical relationships among selected traits and crop predictions. The methodology of plant biometrics was applied to the case of a wine-production vineyard. The chosen biometric traits were vegetation amount, relative altitude in the field, soil compaction, berry size, grape yield, juice PH, and grape sugar content. Reduced soil compaction and low altitudes (higher accumulation of water) were well correlated with large canopies, which in turn indicated higher yield, bigger berry size, and upper acidity. However, the sugar content of the grapes was evenly distributed in the field and therefore uncorrelated to yield, vegetation, altitude, or soil compaction. Various prediction models for grape yield as a function of significant biometric traits are presented as a strategic tool for vineyard growers.
Last update: 19 February 2013