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© 2020 IEEE. In this work, inspired by the needs of the H2020 European Project PANTHEON11http://www.project-pantheon.eu, we address the hazelnut sucker detection and canopy volume estimation problem on a per-plant basis. Sucker control is an essential but challenging practice in agriculture, given the fact that suckers, i.e., shoots that grow from the tree roots, compete with the tree itself for water and nutrients. This research is motivated by the observation that in current best-practice, sucker control is carried out by applying a non-calibrated amount of chemical inputs to each tree. Indeed, a proper sucker detection and estimation algorithm would represent the enabling technology for an environmentally friendly sucker control approach where the amount of herbicide could be properly calibrated according to the needs of each individual plant. In this work, we propose an end-to-end algorithm for detecting the presence of suckers and for estimating their canopy. First a sparse point cloud-based representation of the suckers is detected, then an approximated canopy estimation is achieved by means of a tailored meshing strategy that performs a leaf-based clustering and an iterative clusters connection. The volume is then estimated by the resulting mesh. Preliminary real-world experiments are provided to corroborate the effectiveness of the proposed canopy estimation strategy.

MOTIVATION: Several mathematical models have been investigated for the description of viral dynamics in the human body: HIV-1 infection is a particular and interesting scenario, because the virus attacks cells of the immune system that have a role in the antibody production and its high mutation rate permits to escape both the immune response and, in some cases, the drug pressure. The viral genetic evolution is intrinsically a stochastic process, eventually driven by the drug pressure, dependent on the drug combinations and concentration: in this paper the viral genotypic drug resistance onset is the main focus addressed. The theoretical basis is the modelling of HIV-1 population dynamics as a predator-prey system of differential equations with a time dependent therapy efficacy term, while the viral genome mutation evolution follows a Poisson distribution. The instant probabilities of drug resistance are estimated by means of functions trained from in vitro phenotypes, with a roulette-wheel based mechanisms of resistant selection. Simulations have been designed for treatments made of one and two drugs as well as for combination antiretroviral therapies. The effect of limited adherence to therapy was also analyzed. Sequential treatment change episodes were also exploited with the aim to evaluate optimal synoptic treatment scenarios. RESULTS: The stochastic predator-prey modelling usefully predicted long-term virologic outcomes of evolved HIV-1 strains for selected antiretroviral therapy combinations. For a set of widely used combination therapies, results were consistent with findings reported in literature and with estimates coming from analysis on a large retrospective data base (EuResist)