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Journal of Sensors
Volume 2017 (2017), Article ID 7321950, 12 pages
Research Article

Comparative Analysis between LDR and HDR Images for Automatic Fruit Recognition and Counting

1School of Sciences and Technology, University of Trás-os-Montes and Alto Douro (UTAD), Quinta de Prados, 5000-801 Vila Real, Portugal
2INESC TEC Technology and Science, Campus da FEUP, 4200-465 Porto, Portugal
3Polytechnic Institute of Bragança, School of Technology and Management, Campus de Sta. Apolónia, 5300-253 Bragança, Portugal
4Agricultural School of Jundiaí, Federal University of Rio Grande do Norte (UFRN), Macaíba, RN, Brazil

Correspondence should be addressed to Tatiana M. Pinho

Received 28 April 2017; Revised 20 June 2017; Accepted 3 July 2017; Published 3 August 2017

Academic Editor: Domenico Caputo

Copyright © 2017 Tatiana M. Pinho et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Precision agriculture is gaining an increasing interest in the current farming paradigm. This new production concept relies on the use of information technology (IT) to provide a control and supervising structure that can lead to better management policies. In this framework, imaging techniques that provide visual information over the farming area play an important role in production status monitoring. As such, accurate representation of the gathered production images is a major concern, especially if those images are used in detection and classification tasks. Real scenes, observed in natural environment, present high dynamic ranges that cannot be represented by the common LDR (Low Dynamic Range) devices. However, this issue can be handled by High Dynamic Range (HDR) images since they have the ability to store luminance information similarly to the human visual system. In order to prove their advantage in image processing, a comparative analysis between LDR and HDR images, for fruits detection and counting, was carried out. The obtained results show that the use of HDR images improves the detection performance to more than 30% when compared to LDR.