In this paper, we introduce a new method for the space-time registration of a growing plant that is based on matching the plant at different geometric scales. The proposed method starts with the creation of a topological skeleton of the plant at each time step. This skeleton is then used to segment the plant into parts that we call branches. Then these branches are further divided into smaller segments that possess a simple geometric structure. These segments are matched between two time steps using a random forest classifier based on their topological and geometric features. Then, for each pair of segments matched, a point-wise registration is devised using a non-rigid registration method based on a local ICP. We applied our method to various types of plants, including arabidopsis, tomato plant and maize. We established three different metrics for 3D point-wise shape correspondence to test the accuracy, continuity, and cycle consistency of the mapping. We then compared our method with the state-of-the-art. Our results show that our approach achieves better or similar results with a shorter running time.
Abstract The adoption of agroecological practices will be crucial to address the challenges of climate change and biodiversity loss. Such practices favor the cultivation of plants in complex mixtures with layouts differing from the monoculture approach of conventional agriculture. Inspired by random sequential adsorption processes, we propose a one-dimensional model in which the plants are represented as line segments that start as points and grow at a constant rate until they reach length σ after a time interval τ . The planting positions and times are randomly chosen with the constraint that plant overlap is forbidden. We apply an exact, event-driven simulation to investigate the resulting spatiotemporal patterns and yields in both mono- and duocultures. After a transient period, with oscillations in the density and coverage, the field reaches a steady state in which the mean age of plants is one half of the time to maturity. The structure of the active plants is characterized by correlation functions between the fluctuation of the age of a plant and its k th neighbour. Nearest neighbours are negatively correlated, while next nearest neighbours tend to have similar ages. The steady state yield increases with the planting rate and approaches a maximum value of 4/3 plants per unit length per unit time. For two species with the same size at maturity but different growth rates, the more slowly growing species is enriched in the harvest compared to the seed mix composition. If two species have the same time to maturity but different sizes, the smaller one is enriched in the harvest and, at a sufficiently high planting rate, the larger species may be completely absent. For two species with the same ratio of σ/τ the selectivity is insensitive to the planting rate. This model and the algorithms describing the planting strategies may be extended to higher dimensions, more species and other planting strategies that may assist in the design of novel microfarms.
The dataset gathers points of 5 arabidopsis acquired at consecutive points in time (twice a day). The point cloud is generated using space carving based on 72 images which are acquired using a robotic arm moving around the plant. The soil base is removed using a height threshold. The plant is centered at (0,0,0) and voxel-based downsampled to a unified scale. For most plants, a topological skeleton is extracted and stored as a graph in the file: "skeleton_XXX_connected.txt" And then the skeleton is segmented into different edges. The segmented skeletons' nodes are stored in "skeleton_XXX_noted.csv" This dataset was collected as part of the ROMI project. This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 773875 The link to ROMI project: https://romi-project.eu/
published or not.The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Probing models of minimal swimming vehicules in vivowith microalgae phototaxis.
Many cognitive tasks require the ability to maintain and manipulate simultaneously several chunks of information. Numerous neurobiological observations have reported that this ability, known as the working memory, is associated with both a slow oscillation (leading to the up and down states) and the presence of the theta rhythm. Furthermore, during resting state, the spontaneous activity of the cortex exhibits exquisite spatiotemporal patterns sharing similar features with the ones observed during specific memory tasks. Here to enlighten neural implication of working memory under these complicated dynamics, we propose a phenomenological network model with biologically plausible neural dynamics and recurrent connections. Each unit embeds an internal oscillation at the theta rhythm which can be triggered during up-state of the membrane potential. As a result, the resting state of a single unit is no longer a classical fixed point attractor but rather the Milnor attractor, and multiple oscillations appear in the dynamics of a coupled system. In conclusion, the interplay between the up and down states and theta rhythm endows high potential in working memory operation associated with complexity in spontaneous activities.
