@article {aGene-Mola, title = {Multi-modal Deep Learning for Fuji Apple Detection Using RGB-D Cameras and their Radiometric Capabilities}, journal = {Computers and Electronics in Agriculture}, volume = {162}, year = {2019}, month = {07/2019}, chapter = {689-698}, 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, providethe backscatter signal intensity. However, this radiometric capability has not been exploited for fruit detection applications. This workpresents the KFuji RGB-DS database, composed of 967 multi-modal images containing a total of 12,839 Fuji apples. Compilation of th\ database allowed a study of the usefulness of fusing RGB-D and radiometric information obtained with Kinect v2 for fruit detection. Todo so, the signal intensity was range corrected to overcome signal attenuation, obtaining an image that was proportional to the reflectanceof the scene. A registration between RGB, depth and intensity images was then carried out. The Faster R-CNN model was adapted foruse with five-channel input images: color (RGB), depth (D) and range-corrected intensity signal (S). Results show an improvement of4.46\% in F1-score when adding depth and range-corrected intensity channels, obtaining an F1-score of 0.898 and an AP of 94.8\% whenall channels are used. From our experimental results, it can be concluded that the radiometric capabilities of ToF sensors give valuableinformation for fruit detection.

}, keywords = {Agricultural robotics, Convolutional Neural Networks, Fruit detection, Fruit reflectance, Multi-modal faster R-CNN, RGB-D}, doi = {10.1016/j.compag.2019.05.016}, author = {Gen{\'e}-Mola, Jordi and Ver{\'o}nica Vilaplana and Rosell-Polo, Joan R. and Morros, J.R. and Ruiz-Hidalgo, J. and Gregorio, Eduard} }