Our understanding of plant-microbe interactions in soil is limited by the difficulty of observing processes at the microscopic scale throughout plants\uffe2\uff80\uff99 large volume of influence. Here, we present the development of 3D live microscopy for resolving plant-microbe interactions across the environment of an entire seedling growing in a transparent soil in tailor-made mesocosms, maintaining physical conditions for the culture of both plants and microorganisms. A tailor made dual-illumination light-sheet system acquired scattering signals from the plant whilst fluorescence signals were captured from transparent soil particles and labelled microorganisms, allowing the generation of quantitative data on samples approximately 3600 mm3in size with as good as 5 \uffce\uffbcm resolution at a rate of up to one scan every 30 minutes. The system tracked the movement ofBacillus subtilispopulations in the rhizosphere of lettuce plants in real time, revealing previously unseen patterns of activity. Motile bacteria favoured small pore spaces over the surface of soil particles, colonising the root in a pulsatile manner. Migrations appeared to be directed towards the root cap, the point \uffe2\uff80\uff9cfirst contact\uffe2\uff80\uff9d, before subsequent colonisation of mature epidermis cells. Our findings show that microscopes dedicated to live environmental studies present an invaluable tool to understand plant-microbe interactions.
", "keywords": ["0301 basic medicine", "570", "Microscopy", "Silicon", "0303 health sciences", "Temperature", "root-microbe interactions", "Equipment Design", "Biological Sciences", "Environment", "15. Life on land", "Plant Roots", "630", "Fluorescence", "Soil", "03 medical and health sciences", "Seedlings", "Calibration", "Rhizosphere", "Image Processing", " Computer-Assisted", "environmental imaging", "rhizosphere", "Soil Microbiology", "Bacillus subtilis", "Lactuca"]}, "links": [{"href": "https://eprints.whiterose.ac.uk/178939/18/e2109176118.full.pdf"}, {"href": "https://pnas.org/doi/pdf/10.1073/pnas.2109176118"}, {"href": "https://doi.org/10.1073/pnas.2109176118"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Proceedings%20of%20the%20National%20Academy%20of%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1073/pnas.2109176118", "name": "item", "description": "10.1073/pnas.2109176118", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1073/pnas.2109176118"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-13T00:00:00Z"}}, {"id": "10.1093/jxb/erab174", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:18:29Z", "type": "Journal Article", "created": "2020-12-03", "title": "Digging roots is easier with AI", "description": "AbstractThe scale of root quantification in research is often limited by the time required for sampling, measurement and processing samples. Recent developments in Convolutional Neural Networks (CNN) have made faster and more accurate plant image analysis possible which may significantly reduce the time required for root measurement, but challenges remain in making these methods accessible to researchers without an in-depth knowledge of Machine Learning. We analyzed root images acquired from three destructive root samplings using the RootPainter CNN-software that features an interface for corrective annotation for easier use. Root scans with and without non-root debris were used to test if training a model, i.e., learning from labeled examples, can effectively exclude the debris by comparing the end-results with measurements from clean images. Root images acquired from soil profile walls and the cross-section of soil cores were also used for training and the derived measurements were compared with manual measurements. After 200 minutes of training on each dataset, significant relationships between manual measurements and RootPainter-derived data were noted for monolith (R2=0.99), profile wall (R2=0.76) and core-break (R2=0.57). The rooting density derived from images with debris was not significantly different from that derived from clean images after processing with RootPainter. Rooting density was also successfully calculated from both profile wall and soil core images, and in each case the gradient of root density with depth was not significantly different from manual counts. Our results demonstrate that the proposed approach using CNN can lead to substantial reductions in root sample processing workloads, increasing the potential scale of future root investigations.
", "keywords": ["0301 basic medicine", "root phenotyping", "profile wall", "root washing", "segmentation", "deep learning", "Convolutional neural network", "04 agricultural and veterinary sciences", "15. Life on land", "Soil", "03 medical and health sciences", "core-break", "monolith", "soil coring", "Image Processing", " Computer-Assisted", "0401 agriculture", " forestry", " and fisheries", "Neural Networks", " Computer", "Software"]}, "links": [{"href": "http://academic.oup.com/jxb/article-pdf/72/13/4680/38807872/erab174.pdf"}, {"href": "https://doi.org/10.1093/jxb/erab174"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Experimental%20Botany", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1093/jxb/erab174", "name": "item", "description": "10.1093/jxb/erab174", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1093/jxb/erab174"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-12-02T00:00:00Z"}}, {"id": "10.1101/2021.02.13.430456", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:18:35Z", "type": "Journal Article", "created": "2021-02-13", "title": "Plant-environment microscopy tracks interactions of Bacillus subtilis with plant roots across the entire rhizosphere", "description": "AbstractOur understanding of plant-microbe interactions in soil is limited by the difficulty of observing processes at the microscopic scale throughout plants\uffe2\uff80\uff99 large volume of influence. Here, we present the development of 3D live microscopy for resolving plant-microbe interactions across the environment of an entire seedling growing in a transparent soil in tailor-made mesocosms, maintaining physical conditions for the culture of both plants and microorganisms. A tailor made dual-illumination light-sheet system acquired scattering signals from the plant whilst fluorescence signals were captured from transparent soil particles and labelled microorganisms, allowing the generation of quantitative data on samples approximately 3600 mm3in size with as good as 5 \uffce\uffbcm resolution at a rate of up to one scan every 30 minutes. The system tracked the movement ofBacillus subtilispopulations in the rhizosphere of lettuce plants in real time, revealing previously unseen patterns of activity. Motile bacteria favoured small pore spaces over the surface of soil particles, colonising the root in a pulsatile manner. Migrations appeared to be directed towards the root cap, the point \uffe2\uff80\uff9cfirst contact\uffe2\uff80\uff9d, before subsequent colonisation of mature epidermis cells. Our findings show that microscopes dedicated to live environmental studies present an invaluable tool to understand plant-microbe interactions.We present RootPainter, a GUI-based software tool for the rapid training of deep neural networks for use in biological image analysis. RootPainter facilitates both fully-automatic and semi-automatic image segmentation. We investigate the effectiveness of RootPainter using three plant image datasets, evaluating its potential for root length extraction from chicory roots in soil, biopore counting and root nodule counting from scanned roots. We also use RootPainter to compare dense annotations to corrective ones which are added during the training based on the weaknesses of the current model.
", "keywords": ["Buildings and machinery", "0301 basic medicine", "phenotyping", "root nodule", "biopore", "interactive machine learning", "Research", "segmentation", "deep learning", "rhizotron", "Breeding and genetics", "Machine Learning", "Soil", "03 medical and health sciences", "Deep Learning", "GUI", "Farm nutrient management", "Image Processing", " Computer-Assisted", "Neural Networks", " Computer"]}, "links": [{"href": "https://www.biorxiv.org/content/10.1101/2020.04.16.044461v1.full.pdf"}, {"href": "https://nph.onlinelibrary.wiley.com/doi/pdf/10.1111/nph.18387"}, {"href": "https://doi.org/10.1111/nph.18387"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/New%20Phytologist", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/nph.18387", "name": "item", "description": "10.1111/nph.18387", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/nph.18387"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-04-18T00:00:00Z"}}, {"id": "10.1371/journal.pone.0125404", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:19:40Z", "type": "Journal Article", "created": "2015-05-06", "title": "The Contribution Of Mangrove Expansion To Salt Marsh Loss On The Texas Gulf Coast", "description": "Landscape-level shifts in plant species distribution and abundance can fundamentally change the ecology of an ecosystem. Such shifts are occurring within mangrove-marsh ecotones, where over the last few decades, relatively mild winters have led to mangrove expansion into areas previously occupied by salt marsh plants. On the Texas (USA) coast of the western Gulf of Mexico, most cases of mangrove expansion have been documented within specific bays or watersheds. Based on this body of relatively small-scale work and broader global patterns of mangrove expansion, we hypothesized that there has been a recent regional-level displacement of salt marshes by mangroves. We classified Landsat-5 Thematic Mapper images using artificial neural networks to quantify black mangrove (Avicennia germinans) expansion and salt marsh (Spartina alterniflora and other grass and forb species) loss over 20 years across the entire Texas coast. Between 1990 and 2010, mangrove area grew by 16.1 km(2), a 74% increase. Concurrently, salt marsh area decreased by 77.8 km(2), a 24% net loss. Only 6% of that loss was attributable to mangrove expansion; most salt marsh was lost due to conversion to tidal flats or water, likely a result of relative sea level rise. Our research confirmed that mangroves are expanding and, in some instances, displacing salt marshes at certain locations. However, this shift is not widespread when analyzed at a larger, regional level. Rather, local, relative sea level rise was indirectly implicated as another important driver causing regional-level salt marsh loss. Climate change is expected to accelerate both sea level rise and mangrove expansion; these mechanisms are likely to interact synergistically and contribute to salt marsh loss.", "keywords": ["Satellite Imagery", "0106 biological sciences", "Science", "Climate Change", "Marshes", "Poaceae", "01 natural sciences", "333", "Image Interpretation", " Computer-Assisted", "11. Sustainability", "14. Life underwater", "Mangrove swamps", "Ecosystem", "0105 earth and related environmental sciences", "Gulf of Mexico", "Artificial neural networks", "Winter", "Q", "R", "15. Life on land", "Texas", "Habitats", "13. Climate action", "Wetlands", "Medicine", "Avicennia", "Seasons", "Research Article"], "contacts": [{"organization": "Armitage, Anna R., Highfield, Wesley E., Brody, Samuel D., Louchouarn, Patrick,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1371/journal.pone.0125404"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PLOS%20ONE", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1371/journal.pone.0125404", "name": "item", "description": "10.1371/journal.pone.0125404", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1371/journal.pone.0125404"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2015-05-06T00:00:00Z"}}, {"id": "2750197518", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:26:07Z", "type": "Journal Article", "created": "2017-08-24", "title": "Sparse Recovery in Magnetic Resonance Imaging With a Markov Random Field Prior", "description": "Recent research in compressed sensing of magnetic resonance imaging (CS-MRI) emphasizes the importance of modeling structured sparsity, either in the acquisition or in the reconstruction stages. Subband coefficients of typical images show certain structural patterns, which can be viewed in terms of fixed groups (like wavelet trees) or statistically (certain configurations are more likely than others). Wavelet tree models have already demonstrated excellent performance in MRI recovery from partial data. However, much less attention has been given in CS-MRI to modeling statistically spatial clustering of subband data, although the potentials of such models have been indicated. In this paper, we propose a practical CS-MRI reconstruction algorithm making use of a Markov random field prior model for spatial clustering of subband coefficients and an efficient optimization approach based on proximal splitting. The results demonstrate an improved reconstruction performance compared with both the standard CS-MRI methods and the recent related methods.", "keywords": ["Mice", "Image Processing", " Computer-Assisted", "0202 electrical engineering", " electronic engineering", " information engineering", "Animals", "Brain", "Humans", "02 engineering and technology", "Magnetic Resonance Imaging", "Algorithms", "Markov Chains"], "contacts": [{"organization": "Marko Pani\u0107, Jan Aelterman, Vladimir Crnojevi\u0107, Aleksandra Pi\u017eurica,", "roles": ["creator"]}]}, "links": [{"href": "http://xplorestaging.ieee.org/ielx7/42/8053927/08016375.pdf?arnumber=8016375"}, {"href": "https://doi.org/2750197518"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Transactions%20on%20Medical%20Imaging", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2750197518", "name": "item", "description": "2750197518", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2750197518"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-10-01T00:00:00Z"}}, {"id": "28858789", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:26:11Z", "type": "Journal Article", "created": "2017-08-24", "title": "Sparse Recovery in Magnetic Resonance Imaging With a Markov Random Field Prior", "description": "Recent research in compressed sensing of magnetic resonance imaging (CS-MRI) emphasizes the importance of modeling structured sparsity, either in the acquisition or in the reconstruction stages. Subband coefficients of typical images show certain structural patterns, which can be viewed in terms of fixed groups (like wavelet trees) or statistically (certain configurations are more likely than others). Wavelet tree models have already demonstrated excellent performance in MRI recovery from partial data. However, much less attention has been given in CS-MRI to modeling statistically spatial clustering of subband data, although the potentials of such models have been indicated. In this paper, we propose a practical CS-MRI reconstruction algorithm making use of a Markov random field prior model for spatial clustering of subband coefficients and an efficient optimization approach based on proximal splitting. The results demonstrate an improved reconstruction performance compared with both the standard CS-MRI methods and the recent related methods.", "keywords": ["Mice", "Image Processing", " Computer-Assisted", "0202 electrical engineering", " electronic engineering", " information engineering", "Animals", "Brain", "Humans", "02 engineering and technology", "Magnetic Resonance Imaging", "Algorithms", "Markov Chains"]}, "links": [{"href": "http://xplorestaging.ieee.org/ielx7/42/8053927/08016375.pdf?arnumber=8016375"}, {"href": "https://doi.org/28858789"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Transactions%20on%20Medical%20Imaging", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "28858789", "name": "item", "description": "28858789", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/28858789"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-10-01T00:00:00Z"}}, {"id": "3130873339", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:26:31Z", "type": "Journal Article", "created": "2021-02-13", "title": "Plant-environment microscopy tracks interactions of Bacillus subtilis with plant roots across the entire rhizosphere", "description": "AbstractOur understanding of plant-microbe interactions in soil is limited by the difficulty of observing processes at the microscopic scale throughout plants\uffe2\uff80\uff99 large volume of influence. Here, we present the development of 3D live microscopy for resolving plant-microbe interactions across the environment of an entire seedling growing in a transparent soil in tailor-made mesocosms, maintaining physical conditions for the culture of both plants and microorganisms. A tailor made dual-illumination light-sheet system acquired scattering signals from the plant whilst fluorescence signals were captured from transparent soil particles and labelled microorganisms, allowing the generation of quantitative data on samples approximately 3600 mm3in size with as good as 5 \uffce\uffbcm resolution at a rate of up to one scan every 30 minutes. The system tracked the movement ofBacillus subtilispopulations in the rhizosphere of lettuce plants in real time, revealing previously unseen patterns of activity. Motile bacteria favoured small pore spaces over the surface of soil particles, colonising the root in a pulsatile manner. Migrations appeared to be directed towards the root cap, the point \uffe2\uff80\uff9cfirst contact\uffe2\uff80\uff9d, before subsequent colonisation of mature epidermis cells. Our findings show that microscopes dedicated to live environmental studies present an invaluable tool to understand plant-microbe interactions.Our understanding of plant-microbe interactions in soil is limited by the difficulty of observing processes at the microscopic scale throughout plants\uffe2\uff80\uff99 large volume of influence. Here, we present the development of 3D live microscopy for resolving plant-microbe interactions across the environment of an entire seedling growing in a transparent soil in tailor-made mesocosms, maintaining physical conditions for the culture of both plants and microorganisms. A tailor made dual-illumination light-sheet system acquired scattering signals from the plant whilst fluorescence signals were captured from transparent soil particles and labelled microorganisms, allowing the generation of quantitative data on samples approximately 3600 mm3in size with as good as 5 \uffce\uffbcm resolution at a rate of up to one scan every 30 minutes. The system tracked the movement ofBacillus subtilispopulations in the rhizosphere of lettuce plants in real time, revealing previously unseen patterns of activity. Motile bacteria favoured small pore spaces over the surface of soil particles, colonising the root in a pulsatile manner. Migrations appeared to be directed towards the root cap, the point \uffe2\uff80\uff9cfirst contact\uffe2\uff80\uff9d, before subsequent colonisation of mature epidermis cells. Our findings show that microscopes dedicated to live environmental studies present an invaluable tool to understand plant-microbe interactions.