IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2024-03-29T10:22:30ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2016-06-15T07:23:45Z2016-06-15T07:23:45Zhttp://eprints.imtlucca.it/id/eprint/3499This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/34992016-06-15T07:23:45ZNetworks of plants: how to measure similarity in vegetable speciesDespite the common misconception of nearly static organisms, plants do interact continuously with the environment and with each other. It is fair to assume that during their evolution they developed particular features to overcome similar problems and to exploit possibilities from environment. In this paper we introduce various quantitative measures based on recent advancements in complex network theory that allow to measure the effective similarities of various species. By using this approach on the similarity in fruit-typology ecological traits we obtain a clear plant classification in a way similar to traditional taxonomic classification. This result is not trivial, since a similar analysis done on the basis of diaspore morphological properties do not provide any clear parameter to classify plants species. Complex network theory can then be used in order to determine which feature amongst many can be used to distinguish scope and possibly evolution of plants. Future uses of this approach range from functional classification to quantitative determination of plant communities in nature.Gianna Vivaldogianna.vivaldo@imtlucca.itElisa MasiCamilla PandolfiStefano MancusoGuido Caldarelliguido.caldarelli@imtlucca.it2016-05-10T09:59:13Z2016-05-10T09:59:13Zhttp://eprints.imtlucca.it/id/eprint/3482This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/34822016-05-10T09:59:13ZLeaf segmentation in plant phenotyping: a collation studyImage-based plant phenotyping is a growing application area of computer vision in agriculture. A key task is the segmentation of all individual leaves in images. Here we focus on the most common rosette model plants, Arabidopsis and young tobacco. Although leaves do share appearance and shape characteristics, the presence of occlusions and variability in leaf shape and pose, as well as imaging conditions, render this problem challenging. The aim of this paper is to compare several leaf segmentation solutions on a unique and first-of-its-kind dataset containing images from typical phenotyping experiments. In particular, we report and discuss methods and findings of a collection of submissions for the first Leaf Segmentation Challenge of the Computer Vision Problems in Plant Phenotyping workshop in 2014. Four methods are presented: three segment leaves by processing the distance transform in an unsupervised fashion, and the other via optimal template selection and Chamfer matching. Overall, we find that although separating plant from background can be accomplished with satisfactory accuracy ( $$>$$ > 90 % Dice score), individual leaf segmentation and counting remain challenging when leaves overlap. Additionally, accuracy is lower for younger leaves. We find also that variability in datasets does affect outcomes. Our findings motivate further investigations and development of specialized algorithms for this particular application, and that challenges of this form are ideally suited for advancing the state of the art. Data are publicly available (online at http://www.plant-phenotyping.org/datasets ) to support future challenges beyond segmentation within this application domain.Hanno ScharrMassimo Minervinimassimo.minervini@imtlucca.itAndrew P. FrenchChristian KlukasDavid M. KramerXiaoming LiuImanol LuengoJean-Michel PapeGerrit PolderDanijela VukadinovicXi YinSotirios A. Tsaftarissotirios.tsaftaris@imtlucca.it