{"type": "FeatureCollection", "features": [{"id": "10.1105/tpc.20.00318", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:17:24Z", "type": "Journal Article", "created": "2020-10-10", "title": "ARADEEPOPSIS, an Automated Workflow for Top-View Plant Phenomics using Semantic Segmentation of Leaf States", "description": "Linking plant phenotype to genotype is a common goal to both plant breeders and geneticists. However, collecting phenotypic data for large numbers of plants remain a bottleneck. Plant phenotyping is mostly image based and therefore requires rapid and robust extraction of phenotypic measurements from image data. However, because segmentation tools usually rely on color information, they are sensitive to background or plant color deviations. We have developed a versatile, fully open-source pipeline to extract phenotypic measurements from plant images in an unsupervised manner. ARADEEPOPSIS (https://github.com/Gregor-Mendel-Institute/aradeepopsis) uses semantic segmentation of top-view images to classify leaf tissue into three categories: healthy, anthocyanin rich, and senescent. This makes it particularly powerful at quantitative phenotyping of different developmental stages, mutants with aberrant leaf color and/or phenotype, and plants growing in stressful conditions. On a panel of 210 natural Arabidopsis (Arabidopsis thaliana) accessions, we were able to not only accurately segment images of phenotypically diverse genotypes but also to identify known loci related to anthocyanin production and early necrosis in genome-wide association analyses. Our pipeline accurately processed images of diverse origin, quality, and background composition, and of a distantly related Brassicaceae. ARADEEPOPSIS is deployable on most operating systems and high-performance computing environments and can be used independently of bioinformatics expertise and resources.", "keywords": ["0301 basic medicine", "0303 health sciences", "Genotype", "Large-Scale Biology Articles", "Arabidopsis", "Computational Biology", "Semantics", "Workflow", "Plant Leaves", "03 medical and health sciences", "Phenotype", "Image Processing", " Computer-Assisted", "Phenomics", "Software", "Genome-Wide Association Study"]}, "links": [{"href": "https://doi.org/10.1105/tpc.20.00318"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/The%20Plant%20Cell", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1105/tpc.20.00318", "name": "item", "description": "10.1105/tpc.20.00318", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1105/tpc.20.00318"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-10-09T00:00:00Z"}}, {"id": "10.1111/wre.12255", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:17:56Z", "type": "Journal Article", "created": "2017-05-25", "title": "Big Data for weed control and crop protection", "description": "Summary

Farmers have access to many data\uffe2\uff80\uff90intensive technologies to help them monitor and control weeds and pests. Data collection, data modelling and analysis, and data sharing have become core challenges in weed control and crop protection. We review the challenges and opportunities of Big Data in agriculture: the nature of data collected, Big Data analytics and tools to present the analyses that allow improved crop management decisions for weed control and crop protection. Big Data storage and querying incurs significant challenges, due to the need to distribute data across several machines, as well as due to constantly growing and evolving data from different sources. Semantic technologies are helpful when data from several sources are combined, which involves the challenge of detecting interactions of potential agronomic importance and establishing relationships between data items in terms of meanings and units. Data ownership is analysed using the ethical matrix method to identify the concerns of farmers, agribusiness owners, consumers and the environment. Big Data analytics models are outlined, together with numerical algorithms for training them. Advances and tools to present processed Big Data in the form of actionable information to farmers are reviewed, and a success story from the Netherlands is highlighted. Finally, it is argued that the potential utility of Big Data for weed control is large, especially for invasive, parasitic and herbicide\uffe2\uff80\uff90resistant weeds. This potential can only be realised when agricultural scientists collaborate with data scientists and when organisational, ethical and legal arrangements of data sharing are established.Farmers have access to many data\uffe2\uff80\uff90intensive technologies to help them monitor and control weeds and pests. Data collection, data modelling and analysis, and data sharing have become core challenges in weed control and crop protection. We review the challenges and opportunities of Big Data in agriculture: the nature of data collected, Big Data analytics and tools to present the analyses that allow improved crop management decisions for weed control and crop protection. Big Data storage and querying incurs significant challenges, due to the need to distribute data across several machines, as well as due to constantly growing and evolving data from different sources. Semantic technologies are helpful when data from several sources are combined, which involves the challenge of detecting interactions of potential agronomic importance and establishing relationships between data items in terms of meanings and units. Data ownership is analysed using the ethical matrix method to identify the concerns of farmers, agribusiness owners, consumers and the environment. Big Data analytics models are outlined, together with numerical algorithms for training them. Advances and tools to present processed Big Data in the form of actionable information to farmers are reviewed, and a success story from the Netherlands is highlighted. Finally, it is argued that the potential utility of Big Data for weed control is large, especially for invasive, parasitic and herbicide\uffe2\uff80\uff90resistant weeds. This potential can only be realised when agricultural scientists collaborate with data scientists and when organisational, ethical and legal arrangements of data sharing are established.