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Multi-modal deep learning for Fuji apple detection using RGB-D cameras and their radiometric capabilities

Author
Gené, J.; Vilaplana, V.; Rosell, J.R.; Morros, J.R.; Ruiz-Hidalgo, J.; Gregorio, E.
Type of activity
Journal article
Journal
Computers and electronics in agriculture
Date of publication
2019-07-01
Volume
162
First page
689
Last page
698
DOI
10.1016/j.compag.2019.05.016
Repository
http://hdl.handle.net/2117/175186 Open in new window
URL
https://www.sciencedirect.com/science/article/pii/S0168169919301413 Open in new window
Abstract
Fruit detection and localization will be essential for future agronomic management of fruit crops, with applications in yield prediction, yield mapping and automated harvesting. RGB-D cameras are promising sensors for fruit detection given that they provide geometrical information with color data. Some of these sensors work on the principle of time-of-flight (ToF) and, besides color and depth, provide the backscatter signal intensity. However, this radiometric capability has not been exploited f...
Citation
Gené, J. [et al.]. Multi-modal deep learning for Fuji apple detection using RGB-D cameras and their radiometric capabilities. "Computers and electronics in agriculture", 1 Juliol 2019, vol. 162, p. 689-698.
Keywords
Agricultural robotics, Convolutional neural networks, Fruit detection, Fruit reflectance, Multi-modal faster R-CNN, RGB-D
Group of research
GPI - Image and Video Processing Group
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center

Participants