Semi-supervised deep learning and low-cost cameras for the semantic segmentation of natural images in viticulture View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2022-06-21

AUTHORS

A. Casado-García, J. Heras, A. Milella, R. Marani

ABSTRACT

Automatic yield monitoring and in-field robotic harvesting by low-cost cameras require object detection and segmentation solutions to tackle the poor quality of natural images and the lack of exactly-labeled datasets of consistent sizes. This work proposed the application of deep learning for semantic segmentation of natural images acquired by a low-cost RGB-D camera in a commercial vineyard. Several deep architectures were trained and compared on 85 labeled images. Three semi-supervised learning methods (PseudoLabeling, Distillation and Model Distillation) were proposed to take advantage of 320 non-annotated images. In these experiments, the DeepLabV3+ architecture with a ResNext50 backbone, trained with the set of labeled images, achieved the best overall accuracy of 84.78%. In contrast, the Manet architecture combined with the EfficientnetB3 backbone reached the highest accuracy for the bunch class (85.69%). The application of semi-supervised learning methods boosted the segmentation accuracy between 5.62 and 6.01%, on average. Further discussions are presented to show the effects of a fine-grained manual image annotation on the accuracy of the proposed methods and to compare time requirements. More... »

PAGES

1-26

References to SciGraph publications

  • 2018-10-06. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation in COMPUTER VISION – ECCV 2018
  • 2018-10-06. BiSeNet: Bilateral Segmentation Network for Real-Time Semantic Segmentation in COMPUTER VISION – ECCV 2018
  • 2015-05-27. Deep learning in NATURE
  • 2010-09-01. Grape clusters and foliage detection algorithms for autonomous selective vineyard sprayer in INTELLIGENT SERVICE ROBOTICS
  • 2021-01-02. Semantic segmentation of citrus-orchard using deep neural networks and multispectral UAV-based imagery in PRECISION AGRICULTURE
  • 2021-04-21. Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments in PRECISION AGRICULTURE
  • 2020-06-25. Deep neural networks for grape bunch segmentation in natural images from a consumer-grade camera in PRECISION AGRICULTURE
  • 2018-09-20. UNet++: A Nested U-Net Architecture for Medical Image Segmentation in DEEP LEARNING IN MEDICAL IMAGE ANALYSIS AND MULTIMODAL LEARNING FOR CLINICAL DECISION SUPPORT
  • 2018-09-27. A Survey on Deep Transfer Learning in ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING – ICANN 2018
  • 2015-11-18. U-Net: Convolutional Networks for Biomedical Image Segmentation in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2015
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11119-022-09929-9

    DOI

    http://dx.doi.org/10.1007/s11119-022-09929-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1148838541


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/07", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Agricultural and Veterinary Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0703", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Crop and Pasture Production", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Department of Mathematics and Computer Science, University of La Rioja, Logro\u00f1o, Spain", 
              "id": "http://www.grid.ac/institutes/grid.119021.a", 
              "name": [
                "Department of Mathematics and Computer Science, University of La Rioja, Logro\u00f1o, Spain"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Casado-Garc\u00eda", 
            "givenName": "A.", 
            "id": "sg:person.014477633333.55", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014477633333.55"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Mathematics and Computer Science, University of La Rioja, Logro\u00f1o, Spain", 
              "id": "http://www.grid.ac/institutes/grid.119021.a", 
              "name": [
                "Department of Mathematics and Computer Science, University of La Rioja, Logro\u00f1o, Spain"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Heras", 
            "givenName": "J.", 
            "id": "sg:person.014067512746.30", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014067512746.30"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Bari, Italy", 
              "id": "http://www.grid.ac/institutes/grid.5326.2", 
              "name": [
                "Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Bari, Italy"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Milella", 
            "givenName": "A.", 
            "id": "sg:person.010132247351.99", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010132247351.99"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Bari, Italy", 
              "id": "http://www.grid.ac/institutes/grid.5326.2", 
              "name": [
                "Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Bari, Italy"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Marani", 
            "givenName": "R.", 
            "id": "sg:person.015540754366.08", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015540754366.08"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/978-3-030-00889-5_1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1107102652", 
              "https://doi.org/10.1007/978-3-030-00889-5_1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11119-021-09806-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1137371122", 
              "https://doi.org/10.1007/s11119-021-09806-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-24574-4_28", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017774818", 
              "https://doi.org/10.1007/978-3-319-24574-4_28"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-030-01234-2_49", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1107454614", 
              "https://doi.org/10.1007/978-3-030-01234-2_49"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-030-01261-8_20", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1107502671", 
              "https://doi.org/10.1007/978-3-030-01261-8_20"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11370-010-0078-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002691537", 
              "https://doi.org/10.1007/s11370-010-0078-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11119-020-09736-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1128772224", 
              "https://doi.org/10.1007/s11119-020-09736-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature14539", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010020120", 
              "https://doi.org/10.1038/nature14539"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11119-020-09777-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1134281680", 
              "https://doi.org/10.1007/s11119-020-09777-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-030-01424-7_27", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1107352239", 
              "https://doi.org/10.1007/978-3-030-01424-7_27"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2022-06-21", 
        "datePublishedReg": "2022-06-21", 
        "description": "Automatic yield monitoring and in-field robotic harvesting by low-cost cameras require object detection and segmentation solutions to tackle the poor quality of natural images and the lack of exactly-labeled datasets of consistent sizes. This work proposed the application of deep learning for semantic segmentation of natural images acquired by a low-cost RGB-D camera in a commercial vineyard. Several deep architectures were trained and compared on 85 labeled images. Three semi-supervised learning methods (PseudoLabeling, Distillation and Model Distillation) were proposed to take advantage of 320 non-annotated images. In these experiments, the DeepLabV3+\u2009architecture with a ResNext50 backbone, trained with the set of labeled images, achieved the best overall accuracy of 84.78%. In contrast, the Manet architecture combined with the EfficientnetB3 backbone reached the highest accuracy for the bunch class (85.69%). The application of semi-supervised learning methods boosted the segmentation accuracy between 5.62 and 6.01%, on average. Further discussions are presented to show the effects of a fine-grained manual image annotation on the accuracy of the proposed methods and to compare time requirements.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s11119-022-09929-9", 
        "isAccessibleForFree": true, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.8586649", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1135929", 
            "issn": [
              "1385-2256", 
              "1573-1618"
            ], 
            "name": "Precision Agriculture", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }
        ], 
        "keywords": [
          "semi-supervised learning method", 
          "low-cost cameras", 
          "natural images", 
          "semantic segmentation", 
          "deep learning", 
          "learning methods", 
          "low-cost RGB-D camera", 
          "semi-supervised deep learning", 
          "manual image annotation", 
          "RGB-D camera", 
          "non-annotated images", 
          "image annotation", 
          "deep architecture", 
          "object detection", 
          "MANET architecture", 
          "robotic harvesting", 
          "segmentation solution", 
          "segmentation accuracy", 
          "best overall accuracy", 
          "time requirements", 
          "architecture", 
          "camera", 
          "high accuracy", 
          "segmentation", 
          "images", 
          "overall accuracy", 
          "accuracy", 
          "learning", 
          "yield monitoring", 
          "DeepLabv3", 
          "dataset", 
          "annotation", 
          "applications", 
          "requirements", 
          "method", 
          "set", 
          "detection", 
          "advantages", 
          "poor quality", 
          "backbone", 
          "consistent size", 
          "monitoring", 
          "solution", 
          "further discussion", 
          "quality", 
          "work", 
          "experiments", 
          "harvesting", 
          "class", 
          "commercial vineyards", 
          "size", 
          "lack", 
          "discussion", 
          "effect", 
          "viticulture", 
          "vineyards", 
          "contrast"
        ], 
        "name": "Semi-supervised deep learning and low-cost cameras for the semantic segmentation of natural images in viticulture", 
        "pagination": "1-26", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1148838541"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s11119-022-09929-9"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s11119-022-09929-9", 
          "https://app.dimensions.ai/details/publication/pub.1148838541"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-10-01T06:49", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20221001/entities/gbq_results/article/article_924.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s11119-022-09929-9"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11119-022-09929-9'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11119-022-09929-9'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11119-022-09929-9'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11119-022-09929-9'


     

    This table displays all metadata directly associated to this object as RDF triples.

    174 TRIPLES      21 PREDICATES      89 URIs      71 LITERALS      4 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s11119-022-09929-9 schema:about anzsrc-for:07
    2 anzsrc-for:0703
    3 schema:author Nc9c635c2531b4376a1c3df8a0a41c33c
    4 schema:citation sg:pub.10.1007/978-3-030-00889-5_1
    5 sg:pub.10.1007/978-3-030-01234-2_49
    6 sg:pub.10.1007/978-3-030-01261-8_20
    7 sg:pub.10.1007/978-3-030-01424-7_27
    8 sg:pub.10.1007/978-3-319-24574-4_28
    9 sg:pub.10.1007/s11119-020-09736-0
    10 sg:pub.10.1007/s11119-020-09777-5
    11 sg:pub.10.1007/s11119-021-09806-x
    12 sg:pub.10.1007/s11370-010-0078-z
    13 sg:pub.10.1038/nature14539
    14 schema:datePublished 2022-06-21
    15 schema:datePublishedReg 2022-06-21
    16 schema:description Automatic yield monitoring and in-field robotic harvesting by low-cost cameras require object detection and segmentation solutions to tackle the poor quality of natural images and the lack of exactly-labeled datasets of consistent sizes. This work proposed the application of deep learning for semantic segmentation of natural images acquired by a low-cost RGB-D camera in a commercial vineyard. Several deep architectures were trained and compared on 85 labeled images. Three semi-supervised learning methods (PseudoLabeling, Distillation and Model Distillation) were proposed to take advantage of 320 non-annotated images. In these experiments, the DeepLabV3+ architecture with a ResNext50 backbone, trained with the set of labeled images, achieved the best overall accuracy of 84.78%. In contrast, the Manet architecture combined with the EfficientnetB3 backbone reached the highest accuracy for the bunch class (85.69%). The application of semi-supervised learning methods boosted the segmentation accuracy between 5.62 and 6.01%, on average. Further discussions are presented to show the effects of a fine-grained manual image annotation on the accuracy of the proposed methods and to compare time requirements.
    17 schema:genre article
    18 schema:isAccessibleForFree true
    19 schema:isPartOf sg:journal.1135929
    20 schema:keywords DeepLabv3
    21 MANET architecture
    22 RGB-D camera
    23 accuracy
    24 advantages
    25 annotation
    26 applications
    27 architecture
    28 backbone
    29 best overall accuracy
    30 camera
    31 class
    32 commercial vineyards
    33 consistent size
    34 contrast
    35 dataset
    36 deep architecture
    37 deep learning
    38 detection
    39 discussion
    40 effect
    41 experiments
    42 further discussion
    43 harvesting
    44 high accuracy
    45 image annotation
    46 images
    47 lack
    48 learning
    49 learning methods
    50 low-cost RGB-D camera
    51 low-cost cameras
    52 manual image annotation
    53 method
    54 monitoring
    55 natural images
    56 non-annotated images
    57 object detection
    58 overall accuracy
    59 poor quality
    60 quality
    61 requirements
    62 robotic harvesting
    63 segmentation
    64 segmentation accuracy
    65 segmentation solution
    66 semantic segmentation
    67 semi-supervised deep learning
    68 semi-supervised learning method
    69 set
    70 size
    71 solution
    72 time requirements
    73 vineyards
    74 viticulture
    75 work
    76 yield monitoring
    77 schema:name Semi-supervised deep learning and low-cost cameras for the semantic segmentation of natural images in viticulture
    78 schema:pagination 1-26
    79 schema:productId Ncba57e9cc8ab4c7c8ee9a3d300bedf90
    80 Nfc57f9de0d8249d6a91e968496ebef54
    81 schema:sameAs https://app.dimensions.ai/details/publication/pub.1148838541
    82 https://doi.org/10.1007/s11119-022-09929-9
    83 schema:sdDatePublished 2022-10-01T06:49
    84 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    85 schema:sdPublisher N94e50eab7bbb4bb2bd0c0a289eb75dab
    86 schema:url https://doi.org/10.1007/s11119-022-09929-9
    87 sgo:license sg:explorer/license/
    88 sgo:sdDataset articles
    89 rdf:type schema:ScholarlyArticle
    90 N15d97d4d79e64c2ab65a0a28c26d1190 rdf:first sg:person.014067512746.30
    91 rdf:rest N4ef471b5ef9c41569e036bfb43dfddb7
    92 N4ef471b5ef9c41569e036bfb43dfddb7 rdf:first sg:person.010132247351.99
    93 rdf:rest Nc1411396667b473aa154f6a01173d7c7
    94 N94e50eab7bbb4bb2bd0c0a289eb75dab schema:name Springer Nature - SN SciGraph project
    95 rdf:type schema:Organization
    96 Nc1411396667b473aa154f6a01173d7c7 rdf:first sg:person.015540754366.08
    97 rdf:rest rdf:nil
    98 Nc9c635c2531b4376a1c3df8a0a41c33c rdf:first sg:person.014477633333.55
    99 rdf:rest N15d97d4d79e64c2ab65a0a28c26d1190
    100 Ncba57e9cc8ab4c7c8ee9a3d300bedf90 schema:name dimensions_id
    101 schema:value pub.1148838541
    102 rdf:type schema:PropertyValue
    103 Nfc57f9de0d8249d6a91e968496ebef54 schema:name doi
    104 schema:value 10.1007/s11119-022-09929-9
    105 rdf:type schema:PropertyValue
    106 anzsrc-for:07 schema:inDefinedTermSet anzsrc-for:
    107 schema:name Agricultural and Veterinary Sciences
    108 rdf:type schema:DefinedTerm
    109 anzsrc-for:0703 schema:inDefinedTermSet anzsrc-for:
    110 schema:name Crop and Pasture Production
    111 rdf:type schema:DefinedTerm
    112 sg:grant.8586649 http://pending.schema.org/fundedItem sg:pub.10.1007/s11119-022-09929-9
    113 rdf:type schema:MonetaryGrant
    114 sg:journal.1135929 schema:issn 1385-2256
    115 1573-1618
    116 schema:name Precision Agriculture
    117 schema:publisher Springer Nature
    118 rdf:type schema:Periodical
    119 sg:person.010132247351.99 schema:affiliation grid-institutes:grid.5326.2
    120 schema:familyName Milella
    121 schema:givenName A.
    122 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010132247351.99
    123 rdf:type schema:Person
    124 sg:person.014067512746.30 schema:affiliation grid-institutes:grid.119021.a
    125 schema:familyName Heras
    126 schema:givenName J.
    127 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014067512746.30
    128 rdf:type schema:Person
    129 sg:person.014477633333.55 schema:affiliation grid-institutes:grid.119021.a
    130 schema:familyName Casado-García
    131 schema:givenName A.
    132 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014477633333.55
    133 rdf:type schema:Person
    134 sg:person.015540754366.08 schema:affiliation grid-institutes:grid.5326.2
    135 schema:familyName Marani
    136 schema:givenName R.
    137 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015540754366.08
    138 rdf:type schema:Person
    139 sg:pub.10.1007/978-3-030-00889-5_1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107102652
    140 https://doi.org/10.1007/978-3-030-00889-5_1
    141 rdf:type schema:CreativeWork
    142 sg:pub.10.1007/978-3-030-01234-2_49 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107454614
    143 https://doi.org/10.1007/978-3-030-01234-2_49
    144 rdf:type schema:CreativeWork
    145 sg:pub.10.1007/978-3-030-01261-8_20 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107502671
    146 https://doi.org/10.1007/978-3-030-01261-8_20
    147 rdf:type schema:CreativeWork
    148 sg:pub.10.1007/978-3-030-01424-7_27 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107352239
    149 https://doi.org/10.1007/978-3-030-01424-7_27
    150 rdf:type schema:CreativeWork
    151 sg:pub.10.1007/978-3-319-24574-4_28 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017774818
    152 https://doi.org/10.1007/978-3-319-24574-4_28
    153 rdf:type schema:CreativeWork
    154 sg:pub.10.1007/s11119-020-09736-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1128772224
    155 https://doi.org/10.1007/s11119-020-09736-0
    156 rdf:type schema:CreativeWork
    157 sg:pub.10.1007/s11119-020-09777-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1134281680
    158 https://doi.org/10.1007/s11119-020-09777-5
    159 rdf:type schema:CreativeWork
    160 sg:pub.10.1007/s11119-021-09806-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1137371122
    161 https://doi.org/10.1007/s11119-021-09806-x
    162 rdf:type schema:CreativeWork
    163 sg:pub.10.1007/s11370-010-0078-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1002691537
    164 https://doi.org/10.1007/s11370-010-0078-z
    165 rdf:type schema:CreativeWork
    166 sg:pub.10.1038/nature14539 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010020120
    167 https://doi.org/10.1038/nature14539
    168 rdf:type schema:CreativeWork
    169 grid-institutes:grid.119021.a schema:alternateName Department of Mathematics and Computer Science, University of La Rioja, Logroño, Spain
    170 schema:name Department of Mathematics and Computer Science, University of La Rioja, Logroño, Spain
    171 rdf:type schema:Organization
    172 grid-institutes:grid.5326.2 schema:alternateName Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Bari, Italy
    173 schema:name Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Bari, Italy
    174 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...