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Publikasjoner

NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.

2021

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Sammendrag

Berry fruits (such as strawberry – Fragaria × ananassa, raspberry – Rubus idaeus, blackberry – Rubus fruticosus, currants – Ribes sp., blueberry – Vaccinium sp., and many others) are known for their health benefits due to their richness in sugars, acids, vitamins, minerals, phenolics, and other nutrients. However, their contents are influenced by various factors, such as species, berry cultivar, ripeness, geographical origin, and growing conditions, and the type of extraction and processing of raw seed material. Generally, the berry industry for juice and fruit-wine production produces vast amounts of by-products (mostly seeds). Since berry seeds contain lipids, these by-products are very interesting as a raw material for oil production. As berry seed oil production generates certain waste, strategies towards reducing and valorizing need to be developed. Unlike beery fruits and berry seed oil, whose composition has been tested many times so far, berry seed oil by-products were the subject of a small number of published papers. Due to chemical richness and heterogeneity, it is expected that berry seed oil by-products to be promising natural bio-resource. Still, it is necessary to consider how many other biologically valuable compounds remain in seed waste.

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Sammendrag

Survey-grade laser scanners suitable for drones (UAV-LS) allow the efficient collection of finely detailed three-dimensional (3D) information on tree structures allowing to resolve the complexity of the forest into discrete individual trees and species as well as into different component of the tree. Current developments are hindered by the limited availability of survey-grade UAV-LS data and by the lack of a publicly available benchmark dataset for developing and validating methods. We present a new benchmarking dataset composed of manually labelled UAV-LS data covering forests in different continents and eco-regions. Such data consists in single-tree point clouds, with each point classified as either stem, branches, and leaves. This benchmark dataset offers new possibilities to develop single-tree segmentation algorithms and validate existing ones.