DOI: 10.3897/rio.12.e188665

Authors: Nathaniel Narra, Grischa Fraumann, Raul Palma, Marcin Płóciennik, Michał Błaszczak, Yuansong Qiao

Abstract: The main goal of 'CAgriLab – Consolidated virtual living lab platform for knowledge sharing and adaption in regenerative agriculture' is to establish a consolidated virtual living lab to enable real-time data sharing and collaboration amongst isolated living labs, lighthouses and farmers for exchanging regenerative agriculture knowledge and practices across diverse environments, leveraging Digital Twin (DT), dataspace, blockchain and artificial intelligence (AI) technologies.The CAgriLab consortium is composed of four organisations from three different EU Member States (Ireland, Poland and Finland). It consists of one university of technology, one university of applied sciences, one research centre and one industrial partner. CAgriLab will leverage existing partner projects to pilot and evaluate a decentralised dataspace platform across three countries, covering different use cases i.e. living labs (Ireland), lighthouses (Finland) and farms (Poland). Three sets of DTs (three countries) will be created and connected by the platform.This article contains an edited version of the original funding proposal. The proposal was evaluated by an International Evaluation Committee and selected for funding by the Call Steering Committee.

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DOI: 10.3897/rio.11.e160367

Authors: Rajapreethi Rajendran, Claus Weiland, Jonas Grieb, Soulaine Theocharides, Sam Leeflang, Wouter Addink, Sharif Islam

Abstract: The Distributed System for Scientific Collections (DiSSCo) is a research infrastructure to integrate European natural science collections (NSCs) digitally. The aim is to facilitate and enhance the access, management and analysis of collection assets in one unified digital collection. The Machine Annotation Services (MAS) are essential components of DiSSCo’s Digital Specimen Architecture (DSArch). These services automate the annotation of digital objects to enable labelling and categorisation of NSC's digital assets.To further advance this, a Machine Learning as a Service (MLaaS) approach was developed which provides researchers with the access to pre-trained machine-learning models for complex tasks, such as instance segmentation and morphological analysis of datasets. MLaaS enhances the DiSSCo’s scalability and flexibility and allows the integration of machine-learning tools in close alignment with the FAIR (Findable, Accessible, Interoperable, Reusable) principles.This study employs DiSSCO's MLaaS framework for the quantitative analysis of herbarium specimens. Machine-learning models, such as Mask R-CNN and YOLO11, are comparatively applied to detect and generate the pixel-level masks of plant organs in herbarium sheets. Subsequently, these models are used to reconstruct the scale in the herbarium sheet and to calculate the surface area of identified plant organs. The determination of quantitative characteristics of plant specimens, such as measuring leaf area or the timestamp of the floral transition, opens up herbarium data for reuse in the large prognosis platforms currently developed in the framework of the Common European Data Spaces. In this way, plant trait data mobilised from natural science collections can improve the predictive capability of the vegetation model components of climate-related data spaces.

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DOI: 10.3897/rio.10.e125192

Authors: Desalegn Chala, Erik Kusch, Claus Weiland, Carrie Andrew, Jonas Grieb, Tuomas Rossi, Tomas Martinovic, Dag Endresen

Abstract: Amidst population growth and climate-driven crop stresses such as drought, extreme weather, fungal and insect pests, as well as various crop diseases, ensuring food security demands innovative strategies. Crop wild relatives (CWR), wild plants in the same genus as the crop as well as wild populations belonging to the same species as the crop, offer novel genetic resources crucial for enhancing crop resilience against these stress factors. Here, we introduce a prototype digital twin (pDT) to aid in searching and utilising CWR genetic resources. Using the MoDGP (Modelling the Germplasm of Interest) tool, the pDT enables mapping geographic areas where stress-tolerant CWR populations can be found. With its graphical user interface, it offers flexibility in selecting genetic resources from CWR tailored to enhance resilience of various crops against diverse stress factors.

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