Suchergebnisse

The PFuji-Size dataset is comprised of a collection of 3D point clouds of Fuji apple trees (Malus domestica Borkh. cv. Fuji) scanned at different maturity stages and annotated for fruit detection and size estimation. Structure-from-motion and multi-view stereo techniques were used to generate the 3D point clouds of 6 complete Fuji apple trees containing a total of 615 apples. The resulting point clouds were 3D segmented by identifying the 3D points corresponding to each apple (3D instance segmentation), obtaining a single point cloud for each apple. All segmented apples were labelled with ground truth diameter annotations. Since the data was acquired in field conditions and at different maturity stages, the set includes different fruit diameters -from 26.9 mm to 94.8 mm- and different fruit occlusion percentages due to foliage. In addition, 25 apples were photographed 360° in laboratory conditions, obtaining high resolution 3D point clouds of this sub-set. To the best of the authors' knowledge, this is the first publicly available dataset for apple size estimation in field conditions. This dataset was used to evaluate different fruit size estimation methods in the research article titled 'In-field apple size estimation using photogrammetry-derived 3D point clouds: comparison of 4 different methods considering fruit occlusion' (Gené-Mola et al., 2021). ; This 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) and the Spanish Ministry of Economy and Competitiveness (project AGL2013-48297-C2-2-R). Part of the work was founded with the Grant RTI2018-094222-B-I00 (PAgFRUIT project) by MCIN/AEI/10.13039/501100011033 and by ``ERDF, a way of making Europe'', by the European Union. The Spanish Ministry of Education is thanked for Mr. J. Gené's pre-doctoral fellowships (FPU15/03355). We would also like to thank Nufri (especially Santiago Salamero and Oriol Morreres) for their support during data acquisition.

This paper describes a new robot specifically developed for topiary trimming and evaluatesits performance through a novel evaluation method. Experiments were carried out in a realgarden letting the robot trim spherical-, cylindrical- and cuboid-shaped boxwood topiaries.The robot's performance was evaluated using a quantitative evaluation method, whichmade accurate 3D point clouds of the bush before and after trimming using photogram-metry. For each point in these point clouds, the Euclidean distance to the ground-truthshape was calculated and classified as correctly trimmed (CT) when the distance waswithin 2 cm of the ground-truth shape. Using this information the percentage of CT pointsbefore and after trimming were compared. Results show a reasonable robot accuracy whentrimming spherical topiaries, reporting a 24 percentage point increase of CT points, goingfrom 27% (before) to 51% (after) trimming of the bush surface. Cylinders and cuboids had alower performance, which reported an increase of CT between 0.2 and 6 percentage point.The performance of the robot was also qualitatively assessed by human observers byscoring topiaries. The highest score of a single bush is a 3.1 out of 5. These results, com-bined with the quantitative evaluation show that the robot can trim autonomously, but itdoes not reach the required accuracy for practical application. Despite this, the evaluationmethod was able to reveal main bottlenecks of the robot in real-world experiments.Together with the robot description, this paper can be used as a guideline for the devel-opment and evaluation of gardening robots. ; The work was performed within the TrimBot2020 project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 688007. The authors wish to thank Dejan Kaljaca for his strong contributions on the arm control, motion planning and the field evaluation experiments. The Spanish Ministry of Education and the Spanish Ministry of Science, Innovation and Universities are thanked for J. Gené's pre-doctoral fellowships FPU15/03355 and EST18/00487, respectively.