{"type": "FeatureCollection", "features": [{"id": "10.1016/j.geoderma.2006.01.004", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:15:47Z", "type": "Journal Article", "created": "2006-03-16", "title": "The Effects Of Erosional And Management History On Soil Organic Carbon Stores In Ephemeral Wetlands Of Hummocky Agricultural Landscapes", "description": "Carbon sequestration by agricultural soils has been widely promoted as a means of mitigating greenhouse gas emissions. In many regions agricultural fields are just one component of a complex landscape matrix and understanding the interactions between agricultural fields and other landscape components such as wetlands is crucial for comprehensive, whole-landscape accounting of soil organic carbon (SOC) change. Our objective was to assess the effects of management and erosional history on SOC storage in wetlands of a typical hummocky agricultural landscape in southern Saskatchewan. Wetlands were classed into three land management groups: native wetlands (i.e., within a native landscape), and uncultivated and cultivated wetlands within an agricultural landscape. Detailed topographic surveys were used to develop a digital elevation model of the sites and landform segmentation algorithms were used to delineate the topographic data into landform elements. SOC density to 45 cm was assessed at seven uncultivated wetlands, seven cultivated wetlands, and twelve native wetlands. Mean SOC density decreased from 175.1 mg ha? 1 to 30 cm (equivalent mass depth) for the native wetlands to 168.6 mg ha? 1 for the uncultivated wetlands and 87.2 mg ha? 1 for the cultivated wetlands in the agricultural field. The SOC density of sediment depositional fans in the uncultivated wetlands is high but the total SOC stored in the fans is low due to their small area. The uncultivated wetlands occupy only 11% of the site but account for approximately 23% of SOC stores. Re-establishing permanent vegetation in the cultivated wetlands could provide maximum C sequestration with minimum energy inputs and a minimum loss of productive acreage but the overall consequences for the gas emissions would have to be carefully assessed.", "keywords": ["2. Zero hunger", "canada", "04 agricultural and veterinary sciences", "15. Life on land", "deposition", "6. Clean water", "redistribution", "storage", "cultivation", "vegetation", "13. Climate action", "landform segmentation procedures", "impact", "0401 agriculture", " forestry", " and fisheries", "saskatchewan", "morainal landscape"]}, "links": [{"href": "https://doi.org/10.1016/j.geoderma.2006.01.004"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoderma", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.geoderma.2006.01.004", "name": "item", "description": "10.1016/j.geoderma.2006.01.004", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.geoderma.2006.01.004"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2006-11-01T00:00:00Z"}}, {"id": "10.1016/j.geoderma.2025.117299", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:15:51Z", "type": "Journal Article", "created": "2025-04-19", "title": "Synchrotron-based 3D X-ray computed tomography reveals root system architecture: Plastic responses to phosphorus placement", "description": "We used synchrotron-based X-ray computed tomography (SRXCT) to visualize root distribution in soil cores. X-ray CT is emerging as a leading technique to study plant roots, but SRXCT offers potential advantages compared with conventional X-ray sources, including producing X-rays of higher intensity that are collimated, monochromatic and tuneable; delivering high-resolution data whilst avoiding issues such as beam-hardening and source divergence. We demonstrate the suitability of SRXCT for observing the root system of wheat plants growing in two soils (Calcisol and Ultisol) in response to placement of different phosphorus fertilisers. To optimize scanning quality, we tested the use of an inverse \u2018mask\u2019 in front of the soil cores to achieve a more uniform attenuation along the sample, thereby avoiding saturation of the detector along the thinnest parts of the soil cores. Secondly, we developed a deep learning approach for segmentation and quantification of root length and diameter. Our results demonstrate the use of SRXCT as a tool for studying root system architecture in soil at high spatial resolution. The SRXCT method marks a new stride towards advancing our understanding of root structures in unprecedented detail, opening further avenues for exploring plant-soil interactions.", "keywords": ["X-ray computed tomography", "Image segmentation", "Plant roots", "Root system architecture", "Soil phosphorus", "Science", "Q", "Root distribution", "Synchrotron"]}, "links": [{"href": "https://doi.org/10.1016/j.geoderma.2025.117299"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoderma", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.geoderma.2025.117299", "name": "item", "description": "10.1016/j.geoderma.2025.117299", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.geoderma.2025.117299"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-05-01T00:00:00Z"}}, {"id": "10.1093/jxb/erab174", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:17:16Z", "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": "10.1109/access.2025.3569213", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:17:25Z", "type": "Journal Article", "created": "2025-05-12", "title": "An Efficient Encoding Spectral Information in Hyperspectral Images for Transfer Learning of Mask R-CNN for Instance Segmentation of Tomato Sepals", "description": "The most vulnerable parts of tomatoes are the tips of the sepals, which are the primary entry points for fungal spores. Their precise segmentation within hyperspectral images (HSIs) plays a pivotal role in the development of automated and non-destructive systems for assessing tomatoes&#x2019; sensitivity to fungal infections. This research addresses the critical need for encoding spectral information in hyperspectral imaging to enhance the efficiency of such automated systems. We investigate four different techniques: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Probabilistic Principal Component Analysis (PPCA), and Non-Negative Matrix Factorization (NMF), to perform transfer learning for tomato sepal instance segmentation using models previously trained on RGB images. A comparative analysis of three Mask Region-based Convolutional Neural Network (Mask R-CNN) backbone models is conducted: the Faster R-CNN, Deformable ConvNet, and Feature Pyramid Network (FPN) on spectral-encoded HSIs of the Brioso tomato variety. The Mask R-CNN with FPN, integrated with the NMF technique achieved the highest level of accuracy, yielding a Mean Average Precision (mAP) of 94.05%. Furthermore, on the second dataset, which included an additional three tomato varieties: Capricia, Provine, and Sao Paolo, the same model achieved mAP score of 86.42% across all tomato varieties with only a single false positive detection. Additionally, we incorporated a custom convolutional layer initialized it with estimated NMF coefficients, and achieved a mAP score of 87.40%. This demonstrates the potential of integrating spectral information encoding with trained deep learning-based instance segmentation models to enable robust and accurate transfer learning for automated agricultural food quality assessments.", "keywords": ["instance segmentation", "deep learning", "Electrical engineering. Electronics. Nuclear engineering", "transfer learning", "tomato", "encoding spectral information in HSI", "Hyperspectral imaging (HSI)", "TK1-9971"]}, "links": [{"href": "https://doi.org/10.1109/access.2025.3569213"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Access", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/access.2025.3569213", "name": "item", "description": "10.1109/access.2025.3569213", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/access.2025.3569213"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-01-01T00:00:00Z"}}, {"id": "10.1109/igarss.2019.8899164", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:17:25Z", "type": "Journal Article", "created": "2019-11-25", "title": "Sensitivity of Sentinel-1 Interferometric Coherence to Crop Structure and Soil Moisture", "description": "This paper investigates the sensitivity of Sentinel-1 (S-1) interferometric coherence to crop structure and near surface soil moisture (SSM) content. The study analyzes a data set collected in 2017 over the Apulian Tavoliere agricultural site (Southern Italy). The data set includes: i) in situ data over more than 600 agricultural fields monitored during the 2017 winter and spring growing seasons; ii) time-series of S-1 IW VV & VH backscatter & interferometric coherence; iii) time series of S-1 SSM maps. The temporal behavior of S-1 coherence and VH backscatter has been assessed over the monitored agricultural fields. Initial results indicate a stronger sensitivity of S-1 coherence than VH backscatter to crop geometric structure. In addition, an analysis at site scale, conducted before and after an important rain event, indicates a change of SSM from 0.18 to 0.30 m3/m3 along with a change of S-1 coherence from 0.61 to 0.53.", "keywords": ["2. Zero hunger", "crop segmentation", "crop segmentation; interferometric coherence; Sentinel-1; soil moisture", "0211 other engineering and technologies", "0202 electrical engineering", " electronic engineering", " information engineering", "Sentinel-1", "interferometric coherence", "02 engineering and technology", "soil moisture", "15. Life on land"]}, "links": [{"href": "http://xplorestaging.ieee.org/ielx7/8891871/8897702/08899164.pdf?arnumber=8899164"}, {"href": "https://doi.org/10.1109/igarss.2019.8899164"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IGARSS%202019%20-%202019%20IEEE%20International%20Geoscience%20and%20Remote%20Sensing%20Symposium", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/igarss.2019.8899164", "name": "item", "description": "10.1109/igarss.2019.8899164", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/igarss.2019.8899164"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-07-01T00:00:00Z"}}, {"id": "10.1111/nph.18387", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:17:54Z", "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": "10.21203/rs.3.rs-4951965/v1", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:18:43Z", "type": "Journal Article", "created": "2024-10-15", "title": "All black: a microplastic extraction combined with colour-based analysis allows identification and characterisation of tire wear particles (TWP) in soils", "description": "<title>Abstract</title>         <p>While tire wear particles (TWP) have been estimated to represent more than 90% of the total microplastic (MP) emitted in European countries and may have environmental health effects, only few data about TWP concentrations and characteristics are available today. The lack of data stems from the fact that no standardized, cost efficient or accessible extraction and identification method is available yet. We present a method allowing the extraction of TWP from soil, performing analysis with a conventional optical microscope and a machine learning approach to identify TWP in soil based on their colour. The lowest size of TWP which could be measured reliably with an acceptable recovery using our experimental set-up was 35 \u00b5m. Further improvements would be possible given more advanced technical infrastructure (higher optical magnification and image quality). Our method showed a mean recovery of 85% in the 35-2000 \u00b5m particle size range and no blank contamination. We tested for possible interference from charcoal (as another black soil component with similar properties) in the soils and found a reduction of the interference from charcoal by 92% during extraction. We applied our method to a highway adjacent soil at 1 m, 2 m, 5 m, and 10 m and detected TWP in all samples with a tendency to higher concentrations at 1 m and 2 m from the road compared to 10 m from the road. The observed TWP concentrations were in the same order of magnitude as what was previously reported in literature in highway adjacent soils. These results demonstrate the potential of the method to provide quantitative data on the occurrence and characteristics of TWP in the environment. The method can be easily implemented in many labs, and help to address our knowledge gap regarding TWP concentrations in soils.</p>", "keywords": ["TP1080-1185", "Segmentation", "TD172-193.5", "Tire wear", "Soil pollution", "Machine learning", "Microplastic", "Methodology", "Polymers and polymer manufacture", "Optical microscopy", "Environmental pollution"]}, "links": [{"href": "https://doi.org/10.21203/rs.3.rs-4951965/v1"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Microplastics%20and%20Nanoplastics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.21203/rs.3.rs-4951965/v1", "name": "item", "description": "10.21203/rs.3.rs-4951965/v1", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.21203/rs.3.rs-4951965/v1"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-15T00:00:00Z"}}, {"id": "10.3390/rs12193228", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:29Z", "type": "Journal Article", "created": "2020-10-05", "title": "Qualifications of Rice Growth Indicators Optimized at Different Growth Stages Using Unmanned Aerial Vehicle Digital Imagery", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The accurate estimation of the key growth indicators of rice is conducive to rice production, and the rapid monitoring of these indicators can be achieved through remote sensing using the commercial RGB cameras of unmanned aerial vehicles (UAVs). However, the method of using UAV RGB images lacks an optimized model to achieve accurate qualifications of rice growth indicators. In this study, we established a correlation between the multi-stage vegetation indices (VIs) extracted from UAV imagery and the leaf dry biomass, leaf area index, and leaf total nitrogen for each growth stage of rice. Then, we used the optimal VI (OVI) method and object-oriented segmentation (OS) method to remove the noncanopy area of the image to improve the estimation accuracy. We selected the OVI and the models with the best correlation for each growth stage to establish a simple estimation model database. The results showed that the OVI and OS methods to remove the noncanopy area can improve the correlation between the key growth indicators and VI of rice. At the tillering stage and early jointing stage, the correlations between leaf dry biomass (LDB) and the Green Leaf Index (GLI) and Red Green Ratio Index (RGRI) were 0.829 and 0.881, respectively; at the early jointing stage and late jointing stage, the coefficient of determination (R2) between the Leaf Area Index (LAI) and Modified Green Red Vegetation Index (MGRVI) was 0.803 and 0.875, respectively; at the early stage and the filling stage, the correlations between the leaf total nitrogen (LTN) and UAV vegetation index and the Excess Red Vegetation Index (ExR) were 0.861 and 0.931, respectively. By using the simple estimation model database established using the UAV-based VI and the measured indicators at different growth stages, the rice growth indicators can be estimated for each stage. The proposed estimation model database for monitoring rice at the different growth stages is helpful for improving the estimation accuracy of the key rice growth indicators and accurately managing rice production.</p></article>", "keywords": ["2. Zero hunger", "object-oriented segmentation method", "optimal index method", "rice", "Science", "Q", "rice; growth indicators; multi-stage vegetation index; unmanned aerial vehicle; optimal index method; object-oriented segmentation method; estimation accuracy", "0211 other engineering and technologies", "04 agricultural and veterinary sciences", "02 engineering and technology", "multi-stage vegetation index", "15. Life on land", "estimation accuracy", "growth indicators", "13. Climate action", "unmanned aerial vehicle", "0401 agriculture", " forestry", " and fisheries"], "contacts": [{"organization": "Zhengchao Qiu, Haitao Xiang, Fei Ma, Changwen Du,", "roles": ["creator"]}]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/19/3228/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/19/3228/pdf"}, {"href": "https://doi.org/10.3390/rs12193228"}, {"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/rs12193228", "name": "item", "description": "10.3390/rs12193228", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs12193228"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-10-03T00:00:00Z"}}, {"id": "10.3390/rs13122261", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:30Z", "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": "10.3390/agriculture15020164", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:19Z", "type": "Journal Article", "created": "2025-01-13", "title": "Comparative Evaluation of AI-Based Multi-Spectral Imaging and PCR-Based Assays for Early Detection of Botrytis cinerea Infection on Pepper Plants", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Pepper production is a critical component of the global agricultural economy, with exports reaching a remarkable $6.9B in 2023. This underscores the crop\u2019s importance as a major economic driver of export revenue for producing nations. Botrytis cinerea, the causative agent of gray mold, significantly impacts crops like fruits and vegetables, including peppers. Early detection of this pathogen is crucial for a reduction in fungicide reliance and economic loss prevention. Traditionally, visual inspection has been a primary method for detection. However, symptoms often appear after the pathogen has begun to spread. This study employs the Deep Learning algorithm YOLO for single-class segmentation on plant images to extract spatial details of pepper leaves. The dataset included hyperspectral images at discrete wavelengths (460 nm, 540 nm, 640 nm, 775 nm, and 875 nm) from derived vegetation indices (CVI, GNDVI, NDVI, NPCI, and PSRI) and from RGB. At an Intersection over Union with a 0.5 threshold, the Mean Average Precision (mAP50) achieved by the leaf-segmentation solution YOLOv11-Small was 86.4%. The extracted leaf segments were processed by multiple Transformer models, each yielding a descriptor. These descriptors were combined in ensemble and classified into three distinct classes using a K-nearest neighbor, a Long Short-Term Memory (LSTM), and a ResNet solution. The Transformer models that comprised the best ensemble classifier were as follows: the Swin-L (P:4 \u00d7 4\u2013W:12 \u00d7 12), the ViT-L (P:16 \u00d7 16), the VOLO (D:5), and the XCIT-L (L:24\u2013P:16 \u00d7 16), with the LSTM-based classification solution on the RGB, CVI, GNDVI, NDVI, and PSRI image sets. The classifier achieved an overall accuracy of 87.42% with an F1-Score of 81.13%. The per-class F1-Scores for the three classes were 85.25%, 66.67%, and 78.26%, respectively. Moreover, for B. cinerea detection during the initial as well as quiescent stages of infection prior to symptom development, qPCR-based methods (RT-qPCR) were used for quantification of in planta fungal biomass and integrated with the findings from the AI approach to offer a comprehensive strategy. The study demonstrates early and accurate detection of B. cinerea on pepper plants by combining segmentation techniques with Transformer model descriptors, ensembled for classification. This approach marks a significant step forward in the detection and management of crop diseases, highlighting the potential to integrate such methods into in situ systems like mobile apps or robots.</p></article>", "keywords": ["vision transformers", "Agriculture (General)", "segmentation", "deep learning", "<i>Botrytis cinerea</i>", "descriptor classification", "image classification", "S1-972"]}, "links": [{"href": "https://www.mdpi.com/2077-0472/15/2/164/pdf"}, {"href": "https://doi.org/10.3390/agriculture15020164"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agriculture", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/agriculture15020164", "name": "item", "description": "10.3390/agriculture15020164", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/agriculture15020164"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-01-13T00:00:00Z"}}, {"id": "10.48550/arxiv.2112.03814", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:43Z", "type": "Report", "created": "2022-05-16", "title": "A Contrastive Distillation Approach for Incremental Semantic Segmentation in Aerial Images", "description": "Open Access12 pages, ICIAP 2021", "keywords": ["FOS: Computer and information sciences", "computer vision", " semantic segmentation", " aerial images", " incremental learning", "Computer Vision and Pattern Recognition (cs.CV)", "Image and Video Processing (eess.IV)", "Computer Science - Computer Vision and Pattern Recognition", "FOS: Electrical engineering", " electronic engineering", " information engineering", "0211 other engineering and technologies", "0202 electrical engineering", " electronic engineering", " information engineering", "02 engineering and technology", "Electrical Engineering and Systems Science - Image and Video Processing"]}, "links": [{"href": "https://iris.polito.it/bitstream/11583/2962571/1/ICIAP_2021_arnaudo_contrastive_distillation_camera_ready.pdf"}, {"href": "https://iris.polito.it/bitstream/11583/2962571/3/978-3-031-06430-2_62.pdf"}, {"href": "https://link.springer.com/content/pdf/10.1007/978-3-031-06430-2"}, {"href": "https://link.springer.com/content/pdf/10.1007/978-3-031-06430-2_62"}, {"href": "https://doi.org/10.48550/arxiv.2112.03814"}, {"rel": "self", "type": "application/geo+json", "title": "10.48550/arxiv.2112.03814", "name": "item", "description": "10.48550/arxiv.2112.03814", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.48550/arxiv.2112.03814"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.13744547", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-21T16:20:26Z", "type": "Software", "title": "Pediatric LGE SAX CMR nnU-Net Segmentation Model", "description": "Overview  This repository contains instructions on downloading and installing a pretrained nnU-Net model [1] for the segmentation of the left myocardium and blood-pool in short-axis Late Gadolinium Enhancement (LGE) Cardiac Magnetic Resonance Images (SAX LGE MRI). The training data consists of 268 phase-sensitive inversion recovery (PSIR) images collected in the MYKKE Registry of children with suspected myocarditis [2], which have been annotated by medical staff at the Charit\u00e9 Berlin as part of the DHZK Shared Expertise Project 81X2100274, as well as 100 training images from the EMIDEC dataset [3], with scar and no-reflow areas counted towards the total myocardial segment.\u00a0  The multi-site, multi-age pediatric training cohort includes images from patients of various ages, from 1.5T and 3.0T scanners, with various voxel-sizes and fields-of-view. LGE is present in a variety of patterns and extents, with some MRI presenting as normal.  The model is intended to be used in the nnUNet eco-system.  Training Data Properties     \u00a0 MYKKE EMIDEC   n 268 100   Pathology myocarditis myocardial infarction (67), healthy (33)   Age, years 13.5\u00b15.0 61.0\u00b112.5   Male, % 197 (73.6) 64 (64.0)   Scanners Siemens, Philips, GE Siemens   Numer of Slices 3-38 5-10   Inplane Voxel Size, mm 1.40\u00b10.33 1.25 - 2.0   Slice Thickness, mm 8.29\u00b11.16 8.0   Slice Distance, mm 9.02\u00b12.06 10.0   Magnetic Field Strength, T 1.5, 3.0 1.5, 3.0   Echo Time, ms 2.6\u00b10.98 1.42   Inversion Time, ms 247.76\u00b1108.5 400   Repetition Time, ms 517.6\u00b1330.6 3.5   Flip Angle, \u00b0 26.82\u00b110.27 20     \u00a0  The model available here is trained on the entire MYKKE and EMIDEC Datasets. For segmentation performance evaluation, we conducted a 5-fold cross-validation, leaving a cross-validation test set from the MYKKE dataset out of the training dataset in each fold.  Performance on MYKKE 5-fold Cross Validation Sets     \u00a0 Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Average   Number of Cases 54 54 54 53 53 268   Males, (%) 37 (68.5)\u00a0 41 (75.9) 42 (77.8)\u00a0 37 (69.8)\u00a0 40 (75.5)\u00a0 197 (73.59)   Age, years 14.00\u00b14.47\u00a0 13.37\u00b14.94\u00a0 13.70\u00b15.20\u00a0 13.15\u00b15.37\u00a0 13.49\u00b15.19\u00a0 13.55\u00b15.01   Dice Myocardium 0.818\u00b10.124\u00a0 0.806\u00b10.136\u00a0 0.792\u00b10.182\u00a0 0.806\u00b10.117\u00a0 0.830\u00b10.090\u00a0 0.810\u00b10.133   Dice LV Blood Pool 0.945\u00b10.062\u00a0 0.941\u00b10.056\u00a0 0.903\u00b10.190\u00a0 0.938\u00b10.058\u00a0 0.944\u00b10.043\u00a0 0.934\u00b10.099     Installation  To use the model, it is necessary to install nnUNet version 2 and set the relevant paths. The steps are outlined below:  nnUNet v2 Installation  Follow the nnUNet v2 installation instructions first. Note in particular the pytorch requirements! An installation from pip (rather than cloning the repository) is sufficient to use the model for inference on your data.  Usage  1. Download the model  Create a directory for the model. If you have worked with nnUNet v2 before, you can use your nnUNet_results directory, instead.  Download the model zip file.  Unzip the file to the directory you created, or your nnUNet_results directory. Set the environment path for your nnUNet to this directory:  \u00a0    export nnUNet_results=/path/to/results_directory   As no training is done, only the results path needs to be set as an environment variable for the nnUNet.  2. Set up datasets  To process your own data with the model, it needs to be brought into the nnUNet specific format first. Details can be found in the usage instructions. For inference only, you do not need the dataset.json metadata file, but the data needs to be in nifti format.  For example, the data to be segmented might be stored like this:    lge_sax \u251c\u2500\u2500 image0_0000.nii.gz \u251c\u2500\u2500 image1_0000.nii.gz \u251c\u2500\u2500 image2_0000.nii.gz \u251c\u2500\u2500 image3_0000.nii.gz \u2514\u2500\u2500 image4_0000.nii.gz   Where each nii.gz archive is a CMR LGE SAX volume (one or more slices). Note that the image suffix '0000' is necessary for the model to work.  3. Run inference  To segment the new data, simply use the command line tools provided by nnUNet:    nnUNetv2_predict -i /path/to/lge_sax -o /path/to/outputs -d 503 -c 2d -f all   The -i argument specifies the location of the input data, -o the directory where the segementation masks will be written to. If it doesn't exist, it will be created. -d, -c and -f specify the model to be used (it is stored under Dataset503_emidecTrainMykke/nnUNetTrainer__nnUNetPlans__2d/fold_all).  4. Output  For each input image, the segmentation mask is saved as a nifti file with the same identifier (without the 0000 suffix) in the output folder specified in the command. The mask labels are 1: Myocardium, 2: Blood-pool.  \u00a0  Acknowledgements  We thank the collaborators in the MYKKE consortium for the provision of data and registry infrastructure. In particular, we thank L\u00e9a Ter-Minassian and Theodor Uden for the segmentation of the MYKKE CMR images.\u00a0  This work is supported by the Deutsches Zentrum f\u00fcr Herz-Kreislauf-Forschung (DZHK) \u2013 Shared Expertise Project 81X2100274.", "keywords": ["Cardiovascular diseases", "Segmentation", "nnU-Net", "LGE", "CMR", "MRI"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.13744547"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.13744547", "name": "item", "description": "10.5281/zenodo.13744547", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.13744547"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-09-16T00:00:00Z"}}, {"id": "10.5281/zenodo.7257774", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:21:20Z", "type": "Report", "title": "DEEP LEARNING INSTANCE LEVEL SEGMENTATION OF TOMATO SEPALS ON HYPERSPECTRAL IMAGES", "description": "Tomatoes are extremely prone to pathogenic fungi infections. During product storage, inadequate temperature and humidity conditions can create ideal settings for the fungi to germinate. The most fragile parts of the tomatoes are the sepals, especially on their tips, which are the main entrance spots for fungal spores. Segmentation of tomato sepals in hyperspectral images is an essential step in designing automated systems for fungal infection sensitivity assessment. Precise segmentation of sepals could contribute to predictive models for early assessment of the risk of undesired fungal occurrence.<br> In this study, we analyze the state-of-the-art deep learning architectures for instance segmentation to identify which architecture provides the most precise tomato sepal segmentation in hyperspectral images.", "keywords": ["hyperspectral images", "instance segmentation", "deep learning"], "contacts": [{"organization": "Grbovi\u0107, \u017deljana, Pani\u0107, Marko, Brdar, Sanja, Echtelt, Esther Hogeveen-Van, Mensink, Manon, Woltering, Ernst Woltering, Chauhan, Aneesh,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7257774"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7257774", "name": "item", "description": "10.5281/zenodo.7257774", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7257774"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-09-26T00:00:00Z"}}, {"id": "10.5281/zenodo.4896828", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:21:10Z", "type": "Report", "title": "Semantic Segmentation of Tomato Sepals on Hyperspectral Images Using Deep Learning", "description": "Open Access{'references': ['Z. Grbovic, M. Panic, O. Marko, S. Brdar and V. Crnojevic: ``Wheat Ear Detection in RGB and Thermal Images Using Deep Neural Networks'', Conference on Machine Learning and Data Mining, MLDM 2019, New York, NY, USA, July 20-25, 2019, Proceedings, Volume II, pp. 875--889, ibai publishing, 2019.', 'Ronneberger, Olaf, Philipp Fischer, and Thomas Brox.  'U-net: Convolutional networks for biomedical image segmentation. ' In International Conference on Medical image computing and computer-assisted intervention, pp. 234-241. Springer, Cham, 2015.', 'Badrinarayanan, V., Kendall, A. and Cipolla, R., 2017. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence, 39(12), pp.2481-2495']}", "keywords": ["deep learning", " hyperspectral imaging", " semantic segmentation", " tomato sepals"], "contacts": [{"organization": "\u017deljana Grbovi\u0107, Brki\u0107, Milica, Pani\u0107, Marko, Brdar, Sanja, Echtelt, Esther Hogeveen-Van, Chauhan, Aneesh,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4896828"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4896828", "name": "item", "description": "10.5281/zenodo.4896828", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4896828"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.4896827", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:21:10Z", "type": "Report", "title": "Semantic Segmentation of Tomato Sepals on Hyperspectral Images Using Deep Learning", "description": "Open Access{'references': ['Z. Grbovic, M. Panic, O. Marko, S. Brdar and V. Crnojevic: ``Wheat Ear Detection in RGB and Thermal Images Using Deep Neural Networks'', Conference on Machine Learning and Data Mining, MLDM 2019, New York, NY, USA, July 20-25, 2019, Proceedings, Volume II, pp. 875--889, ibai publishing, 2019.', 'Ronneberger, Olaf, Philipp Fischer, and Thomas Brox.  'U-net: Convolutional networks for biomedical image segmentation. ' In International Conference on Medical image computing and computer-assisted intervention, pp. 234-241. Springer, Cham, 2015.', 'Badrinarayanan, V., Kendall, A. and Cipolla, R., 2017. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence, 39(12), pp.2481-2495']}", "keywords": ["deep learning", " hyperspectral imaging", " semantic segmentation", " tomato sepals"], "contacts": [{"organization": "\u017deljana Grbovi\u0107, Brki\u0107, Milica, Pani\u0107, Marko, Brdar, Sanja, Echtelt, Esther Hogeveen-Van, Chauhan, Aneesh,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4896827"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4896827", "name": "item", "description": "10.5281/zenodo.4896827", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4896827"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.7257773", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:21:20Z", "type": "Report", "title": "DEEP LEARNING INSTANCE LEVEL SEGMENTATION OF TOMATO SEPALS ON HYPERSPECTRAL IMAGES", "description": "Tomatoes are extremely prone to pathogenic fungi infections. During product storage, inadequate temperature and humidity conditions can create ideal settings for the fungi to germinate. The most fragile parts of the tomatoes are the sepals, especially on their tips, which are the main entrance spots for fungal spores. Segmentation of tomato sepals in hyperspectral images is an essential step in designing automated systems for fungal infection sensitivity assessment. Precise segmentation of sepals could contribute to predictive models for early assessment of the risk of undesired fungal occurrence.<br> In this study, we analyze the state-of-the-art deep learning architectures for instance segmentation to identify which architecture provides the most precise tomato sepal segmentation in hyperspectral images.", "keywords": ["hyperspectral images", "instance segmentation", "deep learning"], "contacts": [{"organization": "\u017deljana Grbovi\u0107, Marko Pani\u0107, Sanja Brdar, Esther Hogeveen-van Echtelt, Manon Mensink, Ernst Woltering Woltering, Aneesh Chauhan,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7257773"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7257773", "name": "item", "description": "10.5281/zenodo.7257773", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7257773"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-09-26T00:00:00Z"}}, {"id": "10.5281/zenodo.8085976", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:21:24Z", "type": "Journal Article", "created": "2020-10-05", "title": "Qualifications of Rice Growth Indicators Optimized at Different Growth Stages Using Unmanned Aerial Vehicle Digital Imagery", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The accurate estimation of the key growth indicators of rice is conducive to rice production, and the rapid monitoring of these indicators can be achieved through remote sensing using the commercial RGB cameras of unmanned aerial vehicles (UAVs). However, the method of using UAV RGB images lacks an optimized model to achieve accurate qualifications of rice growth indicators. In this study, we established a correlation between the multi-stage vegetation indices (VIs) extracted from UAV imagery and the leaf dry biomass, leaf area index, and leaf total nitrogen for each growth stage of rice. Then, we used the optimal VI (OVI) method and object-oriented segmentation (OS) method to remove the noncanopy area of the image to improve the estimation accuracy. We selected the OVI and the models with the best correlation for each growth stage to establish a simple estimation model database. The results showed that the OVI and OS methods to remove the noncanopy area can improve the correlation between the key growth indicators and VI of rice. At the tillering stage and early jointing stage, the correlations between leaf dry biomass (LDB) and the Green Leaf Index (GLI) and Red Green Ratio Index (RGRI) were 0.829 and 0.881, respectively; at the early jointing stage and late jointing stage, the coefficient of determination (R2) between the Leaf Area Index (LAI) and Modified Green Red Vegetation Index (MGRVI) was 0.803 and 0.875, respectively; at the early stage and the filling stage, the correlations between the leaf total nitrogen (LTN) and UAV vegetation index and the Excess Red Vegetation Index (ExR) were 0.861 and 0.931, respectively. By using the simple estimation model database established using the UAV-based VI and the measured indicators at different growth stages, the rice growth indicators can be estimated for each stage. The proposed estimation model database for monitoring rice at the different growth stages is helpful for improving the estimation accuracy of the key rice growth indicators and accurately managing rice production.</p></article>", "keywords": ["2. Zero hunger", "object-oriented segmentation method", "optimal index method", "rice", "Science", "Q", "rice; growth indicators; multi-stage vegetation index; unmanned aerial vehicle; optimal index method; object-oriented segmentation method; estimation accuracy", "0211 other engineering and technologies", "04 agricultural and veterinary sciences", "02 engineering and technology", "multi-stage vegetation index", "15. Life on land", "growth indicators", "13. Climate action", "unmanned aerial vehicle", "0401 agriculture", " forestry", " and fisheries"], "contacts": [{"organization": "Zhengchao Qiu, Haitao Xiang, Fei Ma, Changwen Du,", "roles": ["creator"]}]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/19/3228/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/19/3228/pdf"}, {"href": "https://doi.org/10.5281/zenodo.8085976"}, {"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": "10.5281/zenodo.8085976", "name": "item", "description": "10.5281/zenodo.8085976", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8085976"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-10-03T00:00:00Z"}}, {"id": "3092600768", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:23:07Z", "type": "Journal Article", "created": "2020-10-05", "title": "Qualifications of Rice Growth Indicators Optimized at Different Growth Stages Using Unmanned Aerial Vehicle Digital Imagery", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The accurate estimation of the key growth indicators of rice is conducive to rice production, and the rapid monitoring of these indicators can be achieved through remote sensing using the commercial RGB cameras of unmanned aerial vehicles (UAVs). However, the method of using UAV RGB images lacks an optimized model to achieve accurate qualifications of rice growth indicators. In this study, we established a correlation between the multi-stage vegetation indices (VIs) extracted from UAV imagery and the leaf dry biomass, leaf area index, and leaf total nitrogen for each growth stage of rice. Then, we used the optimal VI (OVI) method and object-oriented segmentation (OS) method to remove the noncanopy area of the image to improve the estimation accuracy. We selected the OVI and the models with the best correlation for each growth stage to establish a simple estimation model database. The results showed that the OVI and OS methods to remove the noncanopy area can improve the correlation between the key growth indicators and VI of rice. At the tillering stage and early jointing stage, the correlations between leaf dry biomass (LDB) and the Green Leaf Index (GLI) and Red Green Ratio Index (RGRI) were 0.829 and 0.881, respectively; at the early jointing stage and late jointing stage, the coefficient of determination (R2) between the Leaf Area Index (LAI) and Modified Green Red Vegetation Index (MGRVI) was 0.803 and 0.875, respectively; at the early stage and the filling stage, the correlations between the leaf total nitrogen (LTN) and UAV vegetation index and the Excess Red Vegetation Index (ExR) were 0.861 and 0.931, respectively. By using the simple estimation model database established using the UAV-based VI and the measured indicators at different growth stages, the rice growth indicators can be estimated for each stage. The proposed estimation model database for monitoring rice at the different growth stages is helpful for improving the estimation accuracy of the key rice growth indicators and accurately managing rice production.</p></article>", "keywords": ["2. Zero hunger", "object-oriented segmentation method", "optimal index method", "rice", "Science", "Q", "rice; growth indicators; multi-stage vegetation index; unmanned aerial vehicle; optimal index method; object-oriented segmentation method; estimation accuracy", "0211 other engineering and technologies", "04 agricultural and veterinary sciences", "02 engineering and technology", "multi-stage vegetation index", "15. Life on land", "estimation accuracy", "growth indicators", "13. Climate action", "unmanned aerial vehicle", "0401 agriculture", " forestry", " and fisheries"], "contacts": [{"organization": "Zhengchao Qiu, Haitao Xiang, Fei Ma, Changwen Du,", "roles": ["creator"]}]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/19/3228/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/19/3228/pdf"}, {"href": "https://doi.org/3092600768"}, {"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": "3092600768", "name": "item", "description": "3092600768", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3092600768"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-10-03T00:00:00Z"}}, {"id": "PMC11525289", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:24:39Z", "type": "Journal Article", "created": "2024-10-15", "title": "All black: a microplastic extraction combined with colour-based analysis allows identification and characterisation of tire wear particles (TWP) in soils", "description": "<title>Abstract</title>         <p>While tire wear particles (TWP) have been estimated to represent more than 90% of the total microplastic (MP) emitted in European countries and may have environmental health effects, only few data about TWP concentrations and characteristics are available today. The lack of data stems from the fact that no standardized, cost efficient or accessible extraction and identification method is available yet. We present a method allowing the extraction of TWP from soil, performing analysis with a conventional optical microscope and a machine learning approach to identify TWP in soil based on their colour. The lowest size of TWP which could be measured reliably with an acceptable recovery using our experimental set-up was 35 \u00b5m. Further improvements would be possible given more advanced technical infrastructure (higher optical magnification and image quality). Our method showed a mean recovery of 85% in the 35-2000 \u00b5m particle size range and no blank contamination. We tested for possible interference from charcoal (as another black soil component with similar properties) in the soils and found a reduction of the interference from charcoal by 92% during extraction. We applied our method to a highway adjacent soil at 1 m, 2 m, 5 m, and 10 m and detected TWP in all samples with a tendency to higher concentrations at 1 m and 2 m from the road compared to 10 m from the road. The observed TWP concentrations were in the same order of magnitude as what was previously reported in literature in highway adjacent soils. These results demonstrate the potential of the method to provide quantitative data on the occurrence and characteristics of TWP in the environment. The method can be easily implemented in many labs, and help to address our knowledge gap regarding TWP concentrations in soils.</p>", "keywords": ["TP1080-1185", "Segmentation", "TD172-193.5", "Tire wear", "Soil pollution", "Machine learning", "Microplastic", "Methodology", "Polymers and polymer manufacture", "Optical microscopy", "Environmental pollution"]}, "links": [{"href": "https://doi.org/PMC11525289"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Microplastics%20and%20Nanoplastics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "PMC11525289", "name": "item", "description": "PMC11525289", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PMC11525289"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-15T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=segmentation&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=segmentation&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=segmentation&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=segmentation&offset=19", "hreflang": "en-US"}], "numberMatched": 19, "numberReturned": 19, "distributedFeatures": [], "timeStamp": "2026-09-22T12:50:27.382284Z"}