{"type": "FeatureCollection", "features": [{"id": "10.5281/zenodo.7948400", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:23:53Z", "type": "Report", "title": "Farm management information systems as tools for revealing management zones inside the fields", "description": "INTRODUCTION and OBJECTIVES: There is a huge need to increase the productivity in agriculture to feed the world\u2019s growing population. However, this increase needs to be achieved in a sustainable way, without jeopardising the ecosystem and environment. Innovations in AgTech are accelerating this process and providing adequate solutions for optimisation of on-field decision-making, but they are often isolated and inaccessible to the farmers. The objective of our work was to design a comprehensive farm management system that takes scientific achievements and enables farmers to use them in their daily operations. MATERIAL and METHOD: In order to digitally transform the Serbian agriculture, we designed AgroSense farm management information system. It was launched in 2017 and has since gathered more than 20,000 users, whose total area equals one fourth of all farmland in Serbia. The platform has a number of modules for weather forecast, historical weather records, digital field books, satellite image processing etc., while the newest addition is the drone image processing module. This module allows 3rd party drone services to scan the fields and upload the data to the platform, after which, the images are processed and analysed. The analysis is directed towards zone management delineation, which is the first step in application of precision agriculture technologies. Zones are detected within the field as areas with homogeneous soil and elevation properties. This is done by applying k-means, an unsupervised machine learning model for clusterisation of data, i.e. pixels in this case. This algorithm minimises the intra-class variance (variance of pixels within the zone) and maximises the inter-class variance (variance between pixels from different classes. This zone delineation can be done on a pixel-level if the objective of zone delineation is e.g. choosing the right locations for soil sampling, or on the level of the tractor swath if the goal is e.g. the variable-rate application of fertiliser. The number of zones and the swath width are variable parameters, left to the user to choose, according to the size of the field, type of the equipment and other factors. RESULTS and CONCLUSIONS: The resulting platform was deployed in 2021 and tested on a number of users. It yielded excellent results and served for optimising the route and sampling location of unmanned ground vehicles (UGVs), characterisation of fields and variable application of fertiliser. Future work includes development of other algorithms for more complex image recognition tasks, such as row detection, leaf area assessment and disease/weed mapping.", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land", "drones; precision agriculture; image processing; machine learning"], "contacts": [{"organization": "Marko, Oskar, Brdar, Sanja, Pani\u0107, Marko, Mini\u0107, Vladan, Pejak, Branislav, Crnojevi\u0107, Vladimir,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7948400"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7948400", "name": "item", "description": "10.5281/zenodo.7948400", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7948400"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-06-16T00:00:00Z"}}, {"id": "10.1007/s11356-017-8823-x", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:15:13Z", "type": "Journal Article", "created": "2017-03-24", "title": "Quantitative characterization of pore structure of several biochars with 3D imaging", "description": "Open Access16 pages, 4 figures. The final publication is available at Springer via http://dx.doi.org/10.1007/s11356-017-8823-x", "keywords": ["x-ray tomography", "Condensed Matter - Materials Science", "soil amendment", "pore structure", "ta1171", "ta1182", "Water", "Materials Science (cond-mat.mtrl-sci)", "FOS: Physical sciences", "04 agricultural and veterinary sciences", "01 natural sciences", "6. Clean water", "Diffusion", "Imaging", " Three-Dimensional", "image analysis", "Charcoal", "Image Processing", " Computer-Assisted", "0401 agriculture", " forestry", " and fisheries", "biochar", "Porosity", "soil amendments", "ta218", "water retention", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://link.springer.com/content/pdf/10.1007/s11356-017-8823-x.pdf"}, {"href": "https://doi.org/10.1007/s11356-017-8823-x"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Science%20and%20Pollution%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s11356-017-8823-x", "name": "item", "description": "10.1007/s11356-017-8823-x", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s11356-017-8823-x"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-03-24T00:00:00Z"}}, {"id": "10.1073/pnas.2109176118", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:18:11Z", "type": "Journal Article", "created": "2021-02-13", "title": "Plant-environment microscopy tracks interactions of Bacillus subtilis with plant roots across the entire rhizosphere", "description": "Abstract<p>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.</p>", "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": "10.3389/fpls.2020.00889", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:20:56Z", "type": "Journal Article", "created": "2020-06-23", "title": "An Optimized in situ Quantification Method of Leaf H2O2 Unveils Interaction Dynamics of Pathogenic and Beneficial Bacteria in Wheat", "description": "Hydrogen peroxide (H2O2) functions as an important signaling molecule in plants during biotic interactions. However, the extent to which H2O2 accumulates during these interactions and its implications in the development of disease symptoms is unclear. In this work, we provide a step-by-step optimized protocol for in situ quantification of relative H2O2 concentrations in wheat leaves infected with the pathogenic bacterium Pseudomonas syringae pv. atrofaciens (Psa), either alone or in the presence of the beneficial bacterium Herbaspirillum seropedicae (RAM10). This protocol involved the use of 3-3'diaminobenzidine (DAB) staining method combined with image processing to conduct deconvolution and downstream analysis of the digitalized leaf image. The application of a linear regression model allowed to relate the intensity of the pixels resulting from DAB staining with a given concentration of H2O2. Decreasing H2O2 accumulation patterns were detected at increasing distances from the site of pathogen infection, and H2O2 concentrations were different depending on the bacterial combinations tested. Notably, Psa-challenged plants in presence of RAM10 accumulated less H2O2 in the leaf and showed reduced necrotic symptoms, pointing to a potential role of RAM10 in reducing pathogen-triggered H2O2 levels in young wheat plants.", "keywords": ["biotic interactions", "0301 basic medicine", "0303 health sciences", "03 medical and health sciences", "color deconvolution", "hydrogen peroxide (H2O2)", "Plant culture", "Plant Science", "3-3\u2032diaminobenzidine (DAB)", "image processing", "SB1-1110"]}, "links": [{"href": "https://doi.org/10.3389/fpls.2020.00889"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Plant%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/fpls.2020.00889", "name": "item", "description": "10.3389/fpls.2020.00889", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fpls.2020.00889"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-23T00:00:00Z"}}, {"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": "Abstract<p>The 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.</p>", "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": "Abstract<p>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.</p", "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/10.1101/2021.02.13.430456"}, {"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": "10.1101/2021.02.13.430456", "name": "item", "description": "10.1101/2021.02.13.430456", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1101/2021.02.13.430456"}, {"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": "10.1105/tpc.20.00318", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:18:38Z", "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": "10.1109/TMI.2017.2743819", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:18:39Z", "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/10.1109/TMI.2017.2743819"}, {"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": "10.1109/TMI.2017.2743819", "name": "item", "description": "10.1109/TMI.2017.2743819", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/TMI.2017.2743819"}, {"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": "10.1109/jstars.2019.2958847", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:18:40Z", "type": "Journal Article", "created": "2020-01-22", "title": "Sentinel-1 InSAR Coherence for Land Cover Mapping: A Comparison of Multiple Feature-Based Classifiers", "description": "Open AccessThis article investigates and demonstrates the suitability of the Sentinel-1 interferometric coherence for land cover and vegetation mapping. In addition, this study analyzes the performance of this feature along with polarization and intensity products according to different classification strategies and algorithms. Seven different classification workflows were evaluated, covering pixel- and object-based analyses, unsupervised and supervised classification, different machine-learning classifiers, and the various effects of distinct input features in the SAR domain\u2014interferometric coherence, backscattered intensities, and polarization. All classifications followed the Corine land cover nomenclature. Three different study areas in Europe were selected during 2015 and 2016 campaigns to maximize diversity of land cover. Overall accuracies (OA), ranging from 70% to 90%, were achieved depending on the study area and methodology, considering between 9 and 15 classes. The best results were achieved in the rather flat area of Do\u00f1ana wetlands National Park in Spain (OA 90%), but even the challenging alpine terrain around the city of Merano in northern Italy (OA 77%) obtained promising results. The overall potential of Sentinel-1 interferometric coherence for land cover mapping was evaluated as very good. In all cases, coherence-based results provided higher accuracies than intensity-based strategies, considering 12 days of temporal sampling of the Sentinel-1 A stack. Both coherence and intensity prove to be complementary observables, increasing the overall accuracies in a combined strategy. The accuracy is expected to increase when Sentinel-1 A/B stacks, i.e., six-day sampling, are considered.", "keywords": ["Teledetecci\u00f3", "550", "Interferometric coherence", "Geophysics. Cosmic physics", "ta1171", "0211 other engineering and technologies", "02 engineering and technology", "01 natural sciences", "land cover mapping", "ta216", "TC1501-1800", "[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing", "SDG 15 - Life on Land", "0105 earth and related environmental sciences", "ta213", "QC801-809", "[SPI.ELEC] Engineering Sciences [physics]/Electromagnetism", "interferometric coherence", "Remote sensing", "synthetic aperture radar (SAR)", "15. Life on land", "[SPI.TRON] Engineering Sciences [physics]/Electronics", "SDG 11 - Sustainable Cities and Communities", "[SPI.TRON]Engineering Sciences [physics]/Electronics", "Ocean engineering", "Synthetic aperture radar (SAR)", "[SPI.ELEC]Engineering Sciences [physics]/Electromagnetism", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria de la telecomunicaci\u00f3::Radiocomunicaci\u00f3 i exploraci\u00f3 electromagn\u00e8tica::Teledetecci\u00f3", ":Enginyeria de la telecomunicaci\u00f3::Radiocomunicaci\u00f3 i exploraci\u00f3 electromagn\u00e8tica::Teledetecci\u00f3 [\u00c0rees tem\u00e0tiques de la UPC]", "13. Climate action", "Teor\u00eda de la Se\u00f1al y Comunicaciones", "Sentinel-1", "[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing", "Land cover mapping", "Copernicus"]}, "links": [{"href": "https://doi.org/10.1109/jstars.2019.2958847"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Journal%20of%20Selected%20Topics%20in%20Applied%20Earth%20Observations%20and%20Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/jstars.2019.2958847", "name": "item", "description": "10.1109/jstars.2019.2958847", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/jstars.2019.2958847"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}, {"id": "10.1109/tmi.2017.2743819", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:18:41Z", "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/10.1109/tmi.2017.2743819"}, {"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": "10.1109/tmi.2017.2743819", "name": "item", "description": "10.1109/tmi.2017.2743819", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/tmi.2017.2743819"}, {"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": "10.1111/nph.18387", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:19:15Z", "type": "Journal Article", "created": "2020-04-18", "title": "RootPainter: deep learning segmentation of biological images with corrective annotation", "description": "<p>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.</p>", "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": "10.3390/rs8110938", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:21:18Z", "type": "Journal Article", "created": "2016-11-11", "title": "Data Service Platform for Sentinel-2 Surface Reflectance and Value-Added Products: System Use and Examples", "description": "<p>This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding value-added products for any land surface on Earth. Using the European Space Agency\uffe2\uff80\uff99s (ESA) Sen2Cor algorithm, the platform processes ESA\uffe2\uff80\uff99s Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data. Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates value-added products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R2 = 0.83) and a RMSE of 0.32 m2/m2 (12% of mean value).</p>", "keywords": ["550", "reflectance", "t\u00e9l\u00e9d\u00e9tection", "Science", "0211 other engineering and technologies", "02 engineering and technology", "7. Clean energy", "remote sensing", "Traitement du signal et de l'image", "atmospheric correction", "remote sensing;sentinel-2;atmospheric correction;Sen2Cor;LAI;broadband HDRF", "[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing", "9. Industry and infrastructure", "sentinel-2", "Q", "Signal and Image processing", "04 agricultural and veterinary sciences", "broadband HDRF", "620", "LAI", "atmosph\u00e8re", "Sen2Cor", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "Sentinel-2", "[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing", "donn\u00e9e satellitaire"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/8/11/938/pdf"}, {"href": "https://doi.org/10.3390/rs8110938"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs8110938", "name": "item", "description": "10.3390/rs8110938", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs8110938"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-11-11T00:00:00Z"}}, {"id": "10.5281/zenodo.11200024", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:22:23Z", "type": "Dataset", "title": "Segmentation of particulate organic matter in X-ray Computed Tomography images of soil aggregates with deep convolutional networks", "description": "unspecifiedThis is a dataset that accompanies the paper entitled \u2018Segmentation of particulate organic matter in X-ray Computed Tomography images of soil aggregates with deep convolutional networks\u2019 by Oliveira, A.B., Bordonal, R.O., Peixinho, A.Z., Carvalho, J.L.N., Ferreira, T.R. The files will be publicly accessible when the paper is published.", "keywords": ["Soil carbon stability", "Image processing", "Interactive machine learning", "Deep learning", "Synchrotron"], "contacts": [{"organization": "Oliveira, Aline, Bordonal, Ricardo, Peixinho, Alan, Carvalho, Jo\u00e3o Luis, Ferreira, Talita,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.11200024"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.11200024", "name": "item", "description": "10.5281/zenodo.11200024", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.11200024"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "10.3390/rs10050761", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:21:15Z", "type": "Journal Article", "created": "2018-05-15", "title": "Unsupervised Classification Algorithm for Early Weed Detection in Row-Crops by Combining Spatial and Spectral Information", "description": "<p>In agriculture, reducing herbicide use is a challenge to reduce health and environmental risks while maintaining production yield and quality. Site-specific weed management is a promising way to reach this objective but requires efficient weed detection methods. In this paper, an automatic image processing has been developed to discriminate between crop and weed pixels combining spatial and spectral information extracted from four-band multispectral images. Image data was captured at 3 m above ground, with a camera (multiSPEC 4C, AIRINOV, Paris) mounted on a pole kept manually. For each image, the field of view was approximately 4 m \uffc3\uff97 3 m and the resolution was 6 mm/pix. The row crop arrangement was first used to discriminate between some crop and weed pixels depending on their location inside or outside of crop rows. Then, these pixels were used to automatically build the training dataset concerning the multispectral features of crop and weed pixel classes. For each image, a specific training dataset was used by a supervised classifier (Support Vector Machine) to classify pixels that cannot be correctly discriminated using only the initial spatial approach. Finally, inter-row pixels were classified as weed and in-row pixels were classified as crop or weed depending on their spectral characteristics. The method was assessed on 14 images captured on maize and sugar beet fields. The contribution of the spatial, spectral and combined information was studied with respect to the classification quality. Our results show the better ability of the spatial and spectral combination algorithm to detect weeds between and within crop rows. They demonstrate the improvement of the weed detection rate and the improvement of its robustness. On all images, the mean value of the weed detection rate was 89% for spatial and spectral combination method, 79% for spatial method, and 75% for spectral method. Moreover, our work shows that the plant in-line sowing can be used to design an automatic image processing and classification algorithm to detect weed without requiring any manual data selection and labelling. Since the method required crop row identification, the method is suitable for wide-row crops and high spatial resolution images (at least 6 mm/pix).</p>", "keywords": ["[SDV.SA]Life Sciences [q-bio]/Agricultural sciences", "2. Zero hunger", "[SDV.SA] Life Sciences [q-bio]/Agricultural sciences", "[SDV]Life Sciences [q-bio]", "weed detection", "SVM", "04 agricultural and veterinary sciences", "spatial information", "15. Life on land", "630", "6. Clean water", "image processing", "[SDV] Life Sciences [q-bio]", "multispectral information", "automatic training data set generation", "automatic training dataset generation", "0401 agriculture", " forestry", " and fisheries", "weed detection;image processing;spatial information;multispectral information;automatic training data set generation", "weed detection; image processing; spatial information; multispectral information; automatic training data set generation; SVM"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/10/5/761/pdf"}, {"href": "https://doi.org/10.3390/rs10050761"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs10050761", "name": "item", "description": "10.3390/rs10050761", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs10050761"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-05-15T00:00:00Z"}}, {"id": "10.3390/rs13122261", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:21:17Z", "type": "Journal Article", "created": "2021-06-09", "title": "DeepIndices: Remote Sensing Indices Based on Approximation of Functions through Deep-Learning, Application to Uncalibrated Vegetation Images", "description": "<p>The form of a remote sensing index is generally empirically defined, whether by choosing specific reflectance bands, equation forms or its coefficients. These spectral indices are used as preprocessing stage before object detection/classification. But no study seems to search for the best form through function approximation in order to optimize the classification and/or segmentation. The objective of this study is to develop a method to find the optimal index, using a statistical approach by gradient descent on different forms of generic equations. From six wavebands images, five equations have been tested, namely: linear, linear ratio, polynomial, universal function approximator and dense morphological. Few techniques in signal processing and image analysis are also deployed within a deep-learning framework. Performances of standard indices and DeepIndices were evaluated using two metrics, the dice (similar to f1-score) and the mean intersection over union (mIoU) scores. The study focuses on a specific multispectral camera used in near-field acquisition of soil and vegetation surfaces. These DeepIndices are built and compared to 89 common vegetation indices using the same vegetation dataset and metrics. As an illustration the most used index for vegetation, NDVI (Normalized Difference Vegetation Indices) offers a mIoU score of 63.98% whereas our best models gives an analytic solution to reconstruct an index with a mIoU of 82.19%. This difference is significant enough to improve the segmentation and robustness of the index from various external factors, as well as the shape of detected elements.</p>", "keywords": ["multi-spectral", "[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing", "multispectral", "Science", "0211 other engineering and technologies", "[SDV.SA.STA] Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture", "02 engineering and technology", "Spectral indice", "Deep-learning", "image; precision agriculture; spectral indices; multi-spectral; deep-learning; vegetation segmentation", "deep-learning", "[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing", "[SDV.SA.STA]Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture", "[SDV.BV]Life Sciences [q-bio]/Vegetal Biology", "[SDV.BV] Life Sciences [q-bio]/Vegetal Biology", "image", "precision agriculture", "Precision agriculture", "Vegetation segmentation", "Multi-spectral", "Q", "04 agricultural and veterinary sciences", "15. Life on land", "004", "Image", "vegetation segmentation", "spectral indices", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/12/2261/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/12/2261/pdf"}, {"href": "https://doi.org/10.3390/rs13122261"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs13122261", "name": "item", "description": "10.3390/rs13122261", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13122261"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-06-09T00:00:00Z"}}, {"id": "10.5220/0009169301030110", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:22:06Z", "type": "Journal Article", "created": "2020-03-19", "title": "Two-step Multi-spectral Registration Via Key-point Detector and Gradient Similarity: Application to Agronomic Scenes for Proxy-sensing", "description": "The potential of multi-spectral images is growing rapidly in precision agriculture, and is currently based on the use of multi-sensor cameras. However, their development usually concerns aerial applications and their parameters are optimized for high altitudes acquisition by drone (UAV \u2248 50 meters) to ensure surface coverage and reduce technical problems. With the recent emergence of terrestrial robots (UGV), their use is diverted for nearby agronomic applications. Making it possible to explore new agronomic applications, maximizing specific traits extraction (spectral index, shape, texture \u2026) which requires high spatial resolution. The problem with these cameras is that all sensors are not aligned and the manufacturers\u2019 methods are not suitable for close-field acquisition, resulting in offsets between spectral images and degrading the quality of extractable informations. We therefore need a solution to accurately align images in such condition. In this study we propose a two-steps method applied to the six-bands Airphen multi-sensor camera with (i) affine correction using pre-calibrated matrix at different heights, the closest transformation can be selected via internal GPS and (ii) perspective correction to refine the previous one, using key-points matching between enhanced gradients of each spectral bands. Nine types of key-point detection algorithms (ORB, GFTT, AGAST, FAST, AKAZE, KAZE, BRISK, SURF, MSER) with three different modalities of parameters were evaluated on their speed and performances, we also defined the best reference spectra on each of them. The results show that GFTT is the most suitable methods for key-point extraction using our enhanced gradients, and the best spectral reference was identified to be the band centered on 570 nm for this one. Without any treatment the initial error is about 62 px, with our method, the remaining residual error is less than 1 px, where the manufacturer\u2019s involves distortions and loss of information with an estimated residual error of approximately 12 px", "keywords": ["03 medical and health sciences", "0302 clinical medicine", "Registration", "Registration", " Multi-spectral imagery", " Precision farming", " Feature descriptor", "0202 electrical engineering", " electronic engineering", " information engineering", "[SDV.SA.STA] Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture", "Precision farming", "Feature descriptor", "Multi-spectral imagery", "02 engineering and technology", "15. Life on land", "[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing"]}, "links": [{"href": "https://doi.org/10.5220/0009169301030110"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Proceedings%20of%20the%2015th%20International%20Joint%20Conference%20on%20Computer%20Vision%2C%20Imaging%20and%20Computer%20Graphics%20Theory%20and%20Applications", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5220/0009169301030110", "name": "item", "description": "10.5220/0009169301030110", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5220/0009169301030110"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}, {"id": "10451/47259", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-29T16:24:59Z", "type": "Journal Article", "created": "2020-06-23", "title": "An Optimized in situ Quantification Method of Leaf H2O2 Unveils Interaction Dynamics of Pathogenic and Beneficial Bacteria in Wheat", "description": "Hydrogen peroxide (H2O2) functions as an important signaling molecule in plants during biotic interactions. However, the extent to which H2O2 accumulates during these interactions and its implications in the development of disease symptoms is unclear. In this work, we provide a step-by-step optimized protocol for in situ quantification of relative H2O2 concentrations in wheat leaves infected with the pathogenic bacterium Pseudomonas syringae pv. atrofaciens (Psa), either alone or in the presence of the beneficial bacterium Herbaspirillum seropedicae (RAM10). This protocol involved the use of 3-3'diaminobenzidine (DAB) staining method combined with image processing to conduct deconvolution and downstream analysis of the digitalized leaf image. The application of a linear regression model allowed to relate the intensity of the pixels resulting from DAB staining with a given concentration of H2O2. Decreasing H2O2 accumulation patterns were detected at increasing distances from the site of pathogen infection, and H2O2 concentrations were different depending on the bacterial combinations tested. Notably, Psa-challenged plants in presence of RAM10 accumulated less H2O2 in the leaf and showed reduced necrotic symptoms, pointing to a potential role of RAM10 in reducing pathogen-triggered H2O2 levels in young wheat plants.", "keywords": ["biotic interactions", "0301 basic medicine", "0303 health sciences", "03 medical and health sciences", "color deconvolution", "hydrogen peroxide (H2O2)", "Plant culture", "Plant Science", "3-3\u2032diaminobenzidine (DAB)", "image processing", "SB1-1110"]}, "links": [{"href": "https://repositorio.ulisboa.pt/bitstream/10451/47259/1/Carril%20et%20al%20Front%20Plant%20Sci%202020.pdf"}, {"href": "https://doi.org/10451/47259"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Plant%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10451/47259", "name": "item", "description": "10451/47259", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10451/47259"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-23T00:00:00Z"}}, {"id": "10.5281/zenodo.4482322", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:23:34Z", "type": "Other", "title": "Video", "description": "Instruction video for recreating research results.", "keywords": ["Image processing", "Tutorial"], "contacts": [{"organization": "Bojana, Ivo\u0161evi\u0107", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4482322"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4482322", "name": "item", "description": "10.5281/zenodo.4482322", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4482322"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-30T00:00:00Z"}}, {"id": "10.5281/zenodo.4482323", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:23:34Z", "type": "Other", "title": "Video", "description": "Instruction video for recreating research results.", "keywords": ["Image processing", "Tutorial"], "contacts": [{"organization": "Bojana, Ivo\u0161evi\u0107", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4482323"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4482323", "name": "item", "description": "10.5281/zenodo.4482323", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4482323"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-30T00:00:00Z"}}, {"id": "10.5281/zenodo.7948399", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:23:53Z", "type": "Report", "title": "Farm management information systems as tools for revealing management zones inside the fields", "description": "INTRODUCTION and OBJECTIVES: There is a huge need to increase the productivity in agriculture to feed the world\u2019s growing population. However, this increase needs to be achieved in a sustainable way, without jeopardising the ecosystem and environment. Innovations in AgTech are accelerating this process and providing adequate solutions for optimisation of on-field decision-making, but they are often isolated and inaccessible to the farmers. The objective of our work was to design a comprehensive farm management system that takes scientific achievements and enables farmers to use them in their daily operations. MATERIAL and METHOD: In order to digitally transform the Serbian agriculture, we designed AgroSense farm management information system. It was launched in 2017 and has since gathered more than 20,000 users, whose total area equals one fourth of all farmland in Serbia. The platform has a number of modules for weather forecast, historical weather records, digital field books, satellite image processing etc., while the newest addition is the drone image processing module. This module allows 3rd party drone services to scan the fields and upload the data to the platform, after which, the images are processed and analysed. The analysis is directed towards zone management delineation, which is the first step in application of precision agriculture technologies. Zones are detected within the field as areas with homogeneous soil and elevation properties. This is done by applying k-means, an unsupervised machine learning model for clusterisation of data, i.e. pixels in this case. This algorithm minimises the intra-class variance (variance of pixels within the zone) and maximises the inter-class variance (variance between pixels from different classes. This zone delineation can be done on a pixel-level if the objective of zone delineation is e.g. choosing the right locations for soil sampling, or on the level of the tractor swath if the goal is e.g. the variable-rate application of fertiliser. The number of zones and the swath width are variable parameters, left to the user to choose, according to the size of the field, type of the equipment and other factors. RESULTS and CONCLUSIONS: The resulting platform was deployed in 2021 and tested on a number of users. It yielded excellent results and served for optimising the route and sampling location of unmanned ground vehicles (UGVs), characterisation of fields and variable application of fertiliser. Future work includes development of other algorithms for more complex image recognition tasks, such as row detection, leaf area assessment and disease/weed mapping.", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land", "drones; precision agriculture; image processing; machine learning"], "contacts": [{"organization": "Marko, Oskar, Brdar, Sanja, Pani\u0107, Marko, Mini\u0107, Vladan, Pejak, Branislav, Crnojevi\u0107, Vladimir,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7948399"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7948399", "name": "item", "description": "10.5281/zenodo.7948399", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7948399"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-06-16T00:00:00Z"}}, {"id": "10.6084/m9.figshare.9942854", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:24:34Z", "type": "Journal Article", "created": "2019-10-05", "title": "Supplementary Information from Stabilizing gold nanoparticles for use in X-ray computed tomography imaging of soil systems", "description": "This investigation establishes a system of gold nanoparticles that show good colloidal stability as an X-ray computed tomography (XCT) contrast agent under soil conditions. Gold nanoparticles offer numerous beneficial traits for experiments in biology including: comparatively minimal phytotoxicity, X-ray attenuation of the material and the capacity for functionalization. However, soil salinity, acidity and surface charges can induce aggregation and destabilize gold nanoparticles, hence in biomedical applications polymer coatings are commonly applied to gold nanoparticles to enhance stability in the <i>in vivo</i> environment. Here we first demonstrate non-coated nanoparticles aggregate in soil-water solutions. We then show coating with a polyethylene glycol (PEG) layer prevents this aggregation. To demonstrate this, PEG-coated nanoparticles were drawn through flow columns containing soil and were shown to be stable; this is in contrast with control experiments using silica and alumina-packed columns. We further determined that a suspension of coated gold nanoparticles which fully saturated soil maintained stability over at least 5 days. Finally, we used time resolved XCT imaging and image based models to approximate nanoparticle diffusion as similar to that of other typical plant nutrients diffusing in water. Together, these results establish the PEGylated gold nanoparticles as potential contrast agents for XCT imaging in soil.", "keywords": ["FOS: Computer and information sciences", "Geophysics", "80106 Image Processing", "Biological Engineering", "FOS: Earth and related environmental sciences"], "contacts": [{"organization": "Scotson, Callum P., Munoz-Hernando, Maria, Duncan, Simon J., Siul A. Ruiz, Keyes, Samuel D., Veelen, Arjen Van, Dunlop, Iain E., Roose, Tiina,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.6084/m9.figshare.9942854"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Royal%20Society%20Open%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.6084/m9.figshare.9942854", "name": "item", "description": "10.6084/m9.figshare.9942854", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.6084/m9.figshare.9942854"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "10.6084/m9.figshare.9942854.v1", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:24:34Z", "type": "Journal Article", "created": "2019-10-05", "title": "Supplementary Information from Stabilizing gold nanoparticles for use in X-ray computed tomography imaging of soil systems", "description": "This investigation establishes a system of gold nanoparticles that show good colloidal stability as an X-ray computed tomography (XCT) contrast agent under soil conditions. Gold nanoparticles offer numerous beneficial traits for experiments in biology including: comparatively minimal phytotoxicity, X-ray attenuation of the material and the capacity for functionalization. However, soil salinity, acidity and surface charges can induce aggregation and destabilize gold nanoparticles, hence in biomedical applications polymer coatings are commonly applied to gold nanoparticles to enhance stability in the <i>in vivo</i> environment. Here we first demonstrate non-coated nanoparticles aggregate in soil-water solutions. We then show coating with a polyethylene glycol (PEG) layer prevents this aggregation. To demonstrate this, PEG-coated nanoparticles were drawn through flow columns containing soil and were shown to be stable; this is in contrast with control experiments using silica and alumina-packed columns. We further determined that a suspension of coated gold nanoparticles which fully saturated soil maintained stability over at least 5 days. Finally, we used time resolved XCT imaging and image based models to approximate nanoparticle diffusion as similar to that of other typical plant nutrients diffusing in water. Together, these results establish the PEGylated gold nanoparticles as potential contrast agents for XCT imaging in soil.", "keywords": ["FOS: Computer and information sciences", "Geophysics", "80106 Image Processing", "Biological Engineering", "FOS: Earth and related environmental sciences"], "contacts": [{"organization": "Scotson, Callum P., Munoz-Hernando, Maria, Duncan, Simon J., Siul A. Ruiz, Keyes, Samuel D., Veelen, Arjen Van, Dunlop, Iain E., Roose, Tiina,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.6084/m9.figshare.9942854.v1"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Royal%20Society%20Open%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.6084/m9.figshare.9942854.v1", "name": "item", "description": "10.6084/m9.figshare.9942854.v1", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.6084/m9.figshare.9942854.v1"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"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": "Abstract<p>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.</p", "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/3130873339"}, {"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": "3130873339", "name": "item", "description": "3130873339", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3130873339"}, {"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": "50|od______2659::75ce3208bbf8bd77cc60507730caaff3", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:27:10Z", "type": "Dataset", "title": "Microfluidic study in a meter-long reactive path reveals how the medium's structural heterogeneity shapes MICP-induced biocementation", "description": "This folder includes processed images and image analysis algorithm for the publication 'Microfluidic study in a meter-long reactive path reveals how the medium\u2019s structural heterogeneity shapes MICP-induced biocementation'.   Each folder contains the results of image processing OBJ.   Color explanation   white: pore fluid, blue: bacteria, red: crystals, black: solid   Numbering   xy shows the id number of position   t shows the id number of the time point   Metadata   Metadata extracted from microscopes during image acquisitions are provided in.txt format   \u00a0   a .txt file is included showing the time of image acquisition in the first column   the excel file shows the distance from injection point where images were captured", "keywords": ["microfluidics", " Microbially Induced Calcite Precipitation", " MICP", " image processing", " structural heterogeneity"], "contacts": [{"organization": "Ariadni Elmaloglou, Dimitrios Terzis, Pietro De Anna, Lyesse Laloui,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/50|od______2659::75ce3208bbf8bd77cc60507730caaff3"}, {"rel": "self", "type": "application/geo+json", "title": "50|od______2659::75ce3208bbf8bd77cc60507730caaff3", "name": "item", "description": "50|od______2659::75ce3208bbf8bd77cc60507730caaff3", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/50|od______2659::75ce3208bbf8bd77cc60507730caaff3"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-07T00:00:00Z"}}, {"id": "50|od______2659::800b05ba312a905fd45a3767b555ffe1", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:27:10Z", "type": "Dataset", "title": "Microfluidic study in a meter-long reactive path reveals how the medium's structural heterogeneity shapes MICP-induced biocementation", "description": "This folder includes processed images and image analysis algorithm for the publication 'Microfluidic study in a meter-long reactive path reveals how the medium\u2019s structural heterogeneity shapes MICP-induced biocementation'. Each folder contains the results of image processing OBJ. Color explanation white: pore fluid, blue: bacteria, red: crystals, black: solid Numbering xy shows the id number of position t shows the id number of the time point Metadata Metadata extracted from microscopes during image acquisitions are provided in.txt format \u00a0 a .txt file is included showing the time of image acquisition in the first column the excel file shows the distance from injection point where images were captured", "keywords": ["microfluidics", " Microbially Induced Calcite Precipitation", " MICP", " image processing", " structural heterogeneity"], "contacts": [{"organization": "Ariadni Elmaloglou, Dimitrios Terzis, Pietro De Anna, Lyesse Laloui,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/50|od______2659::800b05ba312a905fd45a3767b555ffe1"}, {"rel": "self", "type": "application/geo+json", "title": "50|od______2659::800b05ba312a905fd45a3767b555ffe1", "name": "item", "description": "50|od______2659::800b05ba312a905fd45a3767b555ffe1", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/50|od______2659::800b05ba312a905fd45a3767b555ffe1"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-07T00:00:00Z"}}, {"id": "7d17628cc22c5a84c405669037b92bc8", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:27:51Z", "type": "Report", "title": "Characterization of fibronectin networks using graph-based representations of the fibers from 2D confocal images", "description": "Open AccessA major constituent of the Extracellular Matrix is a large protein called the Fibronectin (FN). Cellular FN is organized in fibrillar networks and can be assembled differently in the presence of two Extra Domains, EDA and EDB. Our objective was to develop numerical quantitative biomarkers to characterize the geometrical organization of the four FN variants (that differ by the inclusion/exclusion of EDA/EDB) from 2D confocal microscopy images, and to compare sane and cancerous tissues. First, we showed through two classification pipelines, based on curvelet features and deep learning framework, that the FN variants can be distinguished with a similar performance to that of a human annotator. We constructed a graph-based representation of the fibers, which were detected using Gabor filters. Graphspecific attributes were employed to classify the variants, proving that the graph representation embeds relevant information from the confocal images. Furthermore, we identified various techniques capable to differentiate the graphs, allowing us to compare the FN variants quantitatively and qualitatively. Performance analysis using toy graphs showed that the methods, which are based on graph matching and optimal transport, can meaningfully compare graphs. Using the graph-matching framework, we proposed different methodologies for defining the prototype graph, representative of a certain FN class. Additionally, the graph matching served as a tool to compute parameter deformation maps between the variants. These deformation maps were analyzed in a statistical framework showing whether or not the variation of the parameters can be explained by the variance within the same class.", "keywords": ["Appariement de graphes", "Traitement d\u2019images", "Extracellular matrix", "Statistical parametric maps", "Cartes statistiques des parametres", "Image processing", "Matrice extracellulaire", "Machine learning", "Fibronectine", "Apprentissage machine", "Fibronectin", "Graph-matching", "Cancer", "[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing"], "contacts": [{"organization": "Grapa, Anca-Ioana", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/7d17628cc22c5a84c405669037b92bc8"}, {"rel": "self", "type": "application/geo+json", "title": "7d17628cc22c5a84c405669037b92bc8", "name": "item", "description": "7d17628cc22c5a84c405669037b92bc8", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/7d17628cc22c5a84c405669037b92bc8"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}, {"id": "PMC8640753", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:28:46Z", "type": "Journal Article", "created": "2021-02-13", "title": "Plant-environment microscopy tracks interactions of Bacillus subtilis with plant roots across the entire rhizosphere", "description": "Abstract<p>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.</p", "keywords": ["0301 basic medicine", "570", "Silicon", "Environment", "Plant Roots", "630", "Fluorescence", "Soil", "03 medical and health sciences", "Image Processing", " Computer-Assisted", "Soil Microbiology", "root\u2013microbe interactions", "Microscopy", "0303 health sciences", "Temperature", "root-microbe interactions", "Equipment Design", "Biological Sciences", "15. Life on land", "Seedlings", "Calibration", "Rhizosphere", "environmental imaging", "rhizosphere", "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/PMC8640753"}, {"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": "PMC8640753", "name": "item", "description": "PMC8640753", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PMC8640753"}, {"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": "f30cf414-2a45-4eba-b905-c7c771485c1d", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[11.37, 4.33], [11.37, 6.26], [13.08, 6.26], [13.08, 4.33], [11.37, 4.33]]]}, "properties": {"license": "CC BY", "rights": "Restrictions applied to assure the protection of privacy or intellectual property, and any special restrictions or limitations or warnings on using the resource or metadata. Reports, articles, papers, scientific and non - scientific works of any form, including tables, maps, or any other kind of output, in printed or electronic form, based in whole or in part on the data supplied, must contain an acknowledgement of the form: \"Data reused from the BonaRes Data Centre www.bonares.de. This data were created as part of the ZALF Datenerfassung's research activities.\" Although every care has been taken in preparing and testing the data, the ZALF Datenerfassung and the BonaRes Data Centre cannot guarantee that the data are correct; neither does the ZALF Datenerfassung and the BonaRes Data Centre accept any liability whatsoever for any error, missing data or omission in the data, or for any loss or damage arising from its use. 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Reports, articles, papers, scientific and non - scientific works of any form, including tables, maps, or any other kind of output, in printed or electronic form, based in whole or in part on the data supplied, must contain an acknowledgement of the form: \"Data reused from the BonaRes Data Centre www.bonares.de. This data were created as part of the ZALF Datenerfassung's research activities.\" Although every care has been taken in preparing and testing the data, the ZALF Datenerfassung and the BonaRes Data Centre cannot guarantee that the data are correct; neither does the ZALF Datenerfassung and the BonaRes Data Centre accept any liability whatsoever for any error, missing data or omission in the data, or for any loss or damage arising from its use. The ZALF Datenerfassung and BonaRes Data Centre will not be responsible for any direct or indirect use which might be made of the data.", "updated": "2024-09-30", "type": "Dataset", "created": "2024-08-20", "language": "eng", "title": "MODIS fire data (active fire and burned area) from 2003 to 2023 in Yoko and Nanga-Eboko municipalities in the central region of Cameroon", "description": "In this study, we used the monthly time series of medium spatial resolution remote sensing images (2003 to 2023) operated by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. All images were processed on the Google Earth Engine platform (https://code.earthengine.google.com). GEE is a scalable, cloud-based geospatial retrieval and processing platform that offers free in-cloud data access, processing, and administration. The MODIS product used for burned area assessment was from collection 6.1.  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