Multispecies Fruit Flower Detection Using a Refined Semantic Segmentation Network

In fruit production, critical crop management decisions are guided by bloom intensity, i.e., the number of flowers present in an orchard. Despite its importance, bloom intensity is still typically estimated by means of human visual inspection. Existing automated computer vision systems for flower identification are based on hand-engineered techniques that work only under specific conditions and with limited performance. This letter proposes an automated technique for flower identification that is robust to uncontrolled environments and applicable to different flower species. Our method relies on an end-to-end residual convolutional neural network (CNN) that represents the state-of-the-art in semantic segmentation. To enhance its sensitivity to flowers, we fine-tune this network using a single dataset of apple flower images. Since CNNs tend to produce coarse segmentations, we employ a refinement method to better distinguish between individual flower instances. Without any preprocessing or dataset-specific training, experimental results on images of apple, peach, and pear flowers, acquired under different conditions demonstrate the robustness and broad applicability of our method

Fuji-SfM dataset: A collection of annotated images and point clouds for Fuji apple detection and location using structure-from-motion photogrammetry

The present dataset contains colour images acquired in a commercial Fuji apple orchard (Malus domestica Borkh. cv. Fuji) to reconstruct the 3D model of 11 trees by using structure-from-motion (SfM) photogrammetry. The data provided in this article is related to the research article entitled “Fruit detection and 3D location using instance segmentation neural networks and structure-from-motion photogrammetry” [1]. The Fuji-SfM dataset includes: (1) a set of 288 colour images and the corresponding annotations (apples segmentation masks) for training instance segmentation neural networks such as Mask-RCNN; (2) a set of 582 images defining a motion sequence of the scene which was used to generate the 3D model of 11 Fuji apple trees containing 1455 apples by using SfM; (3) the 3D point cloud of the scanned scene with the corresponding apple positions ground truth in global coordinates. With that, this is the first dataset for fruit detection containing images acquired in a motion sequence to build the 3D model of the scanned trees with SfM and including the corresponding 2D and 3D apple location annotations. This data allows the development, training, and test of fruit detection algorithms either based on RGB images, on coloured point clouds or on the combination of both types of data. Dades primàries associades a l'article http://hdl.handle.net/10459.1/68505This work was partly funded by the Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement de la Generalitat de Catalunya (grant 2017 SGR 646), the Spanish Ministry of Economy and Competitiveness (project AGL2013-48297-C2-2-R) and the Spanish Ministry of Science, Innovation and Universities (project RTI2018-094222-B-I00). Part of the work was also developed within the framework of the project TEC2016-75976-R, financed by the Spanish Ministry of Economy, Industry and Competitiveness and the European Regional Development Fund (ERDF). The Spanish Ministry of Education is thanked for Mr. J. Gené’s pre-doctoral fellowships (FPU15/03355)