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• The range corrected intensity from RGB-D sensors is used for fruit detection. • First multi-modal (color, depth, intensity) fruit detection dataset is presented. • Faster R-CNN object detection network is adapted to be used with 5-channel images. • An improvement of 4.46% in F1-score is achieved when using all modalities. • Results show an F1-score of 0.8983 and a mean average precision of 94.8%. 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 for fruit detection applications. This work presents the KFuji RGB-DS database, composed of 967 multi-modal images containing a total of 12,839 Fuji apples. Compilation of the database allowed a study of the usefulness of fusing RGB-D and radiometric information obtained with Kinect v2 for fruit detection. To do so, the signal intensity was range corrected to overcome signal attenuation, obtaining an image that was proportional to the reflectance of the scene. A registration between RGB, depth and intensity images was then carried out. The Faster R-CNN model was adapted for use with five-channel input images: color (RGB), depth (D) and range-corrected intensity signal (S). Results show an improvement of 4.46% in F1-score when adding depth and range-corrected intensity channels, obtaining an F1-score of 0.898 and an AP of 94.8% when all channels are used. From our experimental results, it can be concluded that the radiometric capabilities of ToF sensors give valuable information for fruit detection. [ABSTRACT FROM AUTHOR]

The development of reliable fruit detection and localization systems provides an opportunity to improve the crop value and management by limiting fruit spoilage and optimised harvesting practices. Most proposed systems for fruit detection are based on RGB cameras and thus are affected by intrinsic constraints, such as variable lighting conditions. This work presents a new technique that uses a mobile terrestrial laser scanner (MTLS) to detect and localise Fuji apples. An experimental test focused on Fuji apple trees (Malus domestica Borkh. cv. Fuji) was carried out. A 3D point cloud of the scene was generated using an MTLS composed of a Velodyne VLP-16 LiDAR sensor synchronised with an RTK-GNSS satellite navigation receiver. A reflectance analysis of tree elements was performed, obtaining mean apparent reflectance values of 28.9%, 29.1%, and 44.3% for leaves, branches and trunks, and apples, respectively. These results suggest that the apparent reflectance parameter (at 905 nm wavelength) can be useful to detect apples. For that purpose, a four-step fruit detection algorithm was developed. By applying this algorithm, a localization success of 87.5%, an identification success of 82.4%, and an F1-score of 0.858 were obtained in relation to the total amount of fruits. These detection rates are similar to those obtained by RGB-based systems, but with the additional advantages of providing direct 3D fruit location information, which is not affected by sunlight variations. From the experimental results, it can be concluded that LiDAR-based technology and, particularly, its reflectance information, has potential for remote apple detection and 3D location. • Apples present higher IR reflectance than leaves and trunks. • A new methodology for fruit detection using an MTLS has been developed. • Results show a success rate of 82.4%, with a 10.4% of false detections. • The methodology is not affected by lighting and it provides apple locations in 3D. [ABSTRACT FROM AUTHOR]