{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:35:51Z","timestamp":1740148551385,"version":"3.37.3"},"reference-count":34,"publisher":"Wiley","license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Sensors"],"published-print":{"date-parts":[[2017]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1155\/2017\/7321950","type":"journal-article","created":{"date-parts":[[2017,8,3]],"date-time":"2017-08-03T17:02:37Z","timestamp":1501779757000},"page":"1-12","source":"Crossref","is-referenced-by-count":4,"title":["Comparative Analysis between LDR and HDR Images for Automatic Fruit Recognition and Counting"],"prefix":"10.1155","volume":"2017","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1403-7221","authenticated-orcid":true,"given":"Tatiana M.","family":"Pinho","sequence":"first","affiliation":[{"name":"School of Sciences and Technology, University of Tr\u00e1s-os-Montes and Alto Douro (UTAD), Quinta de Prados, 5000-801 Vila Real, Portugal"},{"name":"INESC TEC Technology and Science, Campus da FEUP, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7616-1383","authenticated-orcid":true,"given":"Jo\u00e3o Paulo","family":"Coelho","sequence":"additional","affiliation":[{"name":"INESC TEC Technology and Science, Campus da FEUP, 4200-465 Porto, Portugal"},{"name":"Polytechnic Institute of Bragan\u00e7a, School of Technology and Management, Campus de Sta. 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