Precision Agriculture View Homepage


Ontology type: schema:Periodical     


Journal Info

START YEAR

1999

PUBLISHER

Springer US

LANGUAGE

en

HOMEPAGE

https://link.springer.com/journal/11119

Recent publications latest 20 shown

  • 2022-09-22 Assessing the precision irrigation potential for increasing crop yield and water savings through simulation
  • 2022-09-03 Evaluation of different crop model-based approaches for variable rate nitrogen fertilization in winter wheat
  • 2022-09-02 Automatic monitoring of flying vegetable insect pests using an RGB camera and YOLO-SIP detector
  • 2022-08-30 Satellite multispectral indices to estimate canopy parameters and within-field management zones in super-intensive almond orchards
  • 2022-08-29 Correlation of UAV and satellite-derived vegetation indices with cotton physiological parameters and their use as a tool for scheduling variable rate irrigation in cotton
  • 2022-08-27 On exploring bivariate and trivariate maps as visualization tools for spatial associations in digital soil mapping: A focus on soil properties
  • 2022-08-26 A new method based on machine learning to forecast fruit yield using spectrometric data: analysis in a fruit supply chain context
  • 2022-08-25 Utility of visible and near-infrared spectroscopy to predict base neutralizing capacity and lime requirement of quaternary soils
  • 2022-08-24 Multi-species weed density assessment based on semantic segmentation neural network
  • 2022-08-24 Land-forming for irrigation (LFI) on a lowland soil protects rice yields while improving irrigation distribution uniformity
  • 2022-08-21 Early/late fusion structures with optimized feature selection for weed detection using visible and thermal images of paddy fields
  • 2022-08-20 Yield sensing technologies for perennial and annual horticultural crops: a review
  • 2022-08-20 The effect of local samples in the accuracy of mid-infrared (MIR) and X-ray fluorescence (XRF) -based spectral prediction models
  • 2022-08-14 Identification of pathogens in corn using near-infrared UAV imagery and deep learning
  • 2022-08-12 The estimation of wheat tiller number based on UAV images and gradual change features (GCFs)
  • 2022-08-08 Early yield prediction in different grapevine varieties using computer vision and machine learning
  • 2022-08-07 Grape leaf disease identification with sparse data via generative adversarial networks and convolutional neural networks
  • 2022-08-05 Efficient tomato harvesting robot based on image processing and deep learning
  • 2022-08-05 Leaf area index estimations by deep learning models using RGB images and data fusion in maize
  • 2022-08-03 UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat
  • 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", 
        "alternateName": "An International Journal on Advances in Precision Agriculture", 
        "contentRating": [
          {
            "author": "snip", 
            "ratingValue": "1.9500000476837158", 
            "type": "Rating"
          }, 
          {
            "author": "sjr", 
            "ratingValue": "1.1699999570846558", 
            "type": "Rating"
          }
        ], 
        "description": "

    The International Society of Precision Agriculture (https://www.ispag.org/ ) adopted the following definition of precision agriculture in 2019:

    \u00a0\u2018Precision agriculture is a management strategy that gathers, processes and analyzes temporal, spatial and individual data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production.\u2019

    Topics that are addressed in the journal include:

    • Within-field natural resources variability, including soil and crop variability and characteristics
    • Managing variability, including sampling techniques and methods, nutrient and crop protection chemicals recommendation and crop quality
    • Engineering technology, focusing on sensor systems, computational techniques, positioning systems and control systems for site-specific application
    • Adoption and economics of precision agriculture management
    • Environmental coverage including sediments, leaching, runoff and drainage related to within-field spatial variability.

    Precision Agriculture:

    • Presents the most innovative results emerging from research in the field
    • Provides an effective forum for disseminating original and fundamental research and experience in this rapidly advancing field
    • Submissions are encouraged on measurement, management, technology and impact of spatial variability at the within-field scale
    ", "editor": [ { "familyName": "Stafford", "givenName": "John V.", "type": "Person" } ], "id": "sg:journal.1135929", "inLanguage": [ "en" ], "isAccessibleForFree": false, "issn": [ "1385-2256", "1573-1618" ], "license": "Hybrid", "name": "Precision Agriculture", "productId": [ { "name": "dimensions_id", "type": "PropertyValue", "value": [ "135929" ] }, { "name": "lccn_id", "type": "PropertyValue", "value": [ "2004229332" ] }, { "name": "nlm_unique_id", "type": "PropertyValue", "value": [ "101775168" ] }, { "name": "nsd_ids_id", "type": "PropertyValue", "value": [ "445998" ] }, { "name": "era_ids_id", "type": "PropertyValue", "value": [ "5325" ] } ], "publisher": { "name": "Springer US", "type": "Organization" }, "publisherImprint": "Springer", "sameAs": [ "https://app.dimensions.ai/discover/publication?and_facet_source_title=jour.1135929" ], "sdDataset": "journals", "sdDatePublished": "2022-10-01T07:03", "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/journal/journal_0.jsonl", "startYear": "1999", "type": "Periodical", "url": "https://link.springer.com/journal/11119" } ]
     

    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/journal.1135929'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/journal.1135929'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/journal.1135929'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/journal.1135929'


     

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

    61 TRIPLES      21 PREDICATES      28 URIs      24 LITERALS      10 BLANK NODES

    Subject Predicate Object
    1 sg:journal.1135929 schema:alternateName An International Journal on Advances in Precision Agriculture
    2 schema:contentRating N0622b34fbb094bb583d1666823865209
    3 Nac4f9e0ddf5649138c5f02f71aa2634a
    4 schema:description <p>The International Society of Precision Agriculture (https://www.ispag.org/ ) adopted the following definition of precision agriculture in 2019:</p><p> ‘<i>Precision agriculture is a management strategy that gathers, processes and analyzes temporal, spatial and individual data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production.</i>’</p><p>Topics that are addressed in the journal include:</p><ul><li>Within-field natural resources variability, including soil and crop variability and characteristics</li><li>Managing variability, including sampling techniques and methods, nutrient and crop protection chemicals recommendation and crop quality</li><li>Engineering technology, focusing on sensor systems, computational techniques, positioning systems and control systems for site-specific application</li><li>Adoption and economics of precision agriculture management</li><li>Environmental coverage including sediments, leaching, runoff and drainage related to within-field spatial variability.</li></ul><p><i>Precision Agriculture:</i></p><ul><li>Presents the most innovative results emerging from research in the field </li><li>Provides an effective forum for disseminating original and fundamental research and experience in this rapidly advancing field</li><li>Submissions are encouraged on measurement, management, technology and impact of spatial variability at the within-field scale</li></ul>
    5 schema:editor Nce2df2514bbb4cbe8310d5e6414b422a
    6 schema:inLanguage en
    7 schema:isAccessibleForFree false
    8 schema:issn 1385-2256
    9 1573-1618
    10 schema:license Hybrid
    11 schema:name Precision Agriculture
    12 schema:productId N6349fb8e80e6418d80387121b2440e80
    13 Nbbe0ee6707bc46a0bf978cfe9c01bfc5
    14 Nbc89881c3b5f48e99d9644daacdbc41a
    15 Nbd19a5eb91d9447fbc2700993a298bff
    16 Nda07d8a50d554aa099c3717f1a32645b
    17 schema:publisher N27d6806a947c4cf3992bfb9f8e4a3dd4
    18 schema:publisherImprint Springer
    19 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_source_title=jour.1135929
    20 schema:sdDatePublished 2022-10-01T07:03
    21 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    22 schema:sdPublisher Nb7d69676fa9940b7a0dc9c6dcd6cbda6
    23 schema:startYear 1999
    24 schema:url https://link.springer.com/journal/11119
    25 sgo:license sg:explorer/license/
    26 sgo:sdDataset journals
    27 rdf:type schema:Periodical
    28 N0622b34fbb094bb583d1666823865209 schema:author N58883515e3a449a59ef4fb33a36c7ded
    29 schema:ratingValue 1.1699999570846558
    30 rdf:type schema:Rating
    31 N27d6806a947c4cf3992bfb9f8e4a3dd4 schema:name Springer US
    32 rdf:type schema:Organization
    33 N58883515e3a449a59ef4fb33a36c7ded rdf:first sjr
    34 rdf:rest rdf:nil
    35 N6349fb8e80e6418d80387121b2440e80 schema:name nlm_unique_id
    36 schema:value 101775168
    37 rdf:type schema:PropertyValue
    38 N7b348ff48ffc46b1bc699620022105df rdf:first snip
    39 rdf:rest rdf:nil
    40 N886d3fc37e8149898b746a0ba3085dc1 schema:familyName Stafford
    41 schema:givenName John V.
    42 rdf:type schema:Person
    43 Nac4f9e0ddf5649138c5f02f71aa2634a schema:author N7b348ff48ffc46b1bc699620022105df
    44 schema:ratingValue 1.9500000476837158
    45 rdf:type schema:Rating
    46 Nb7d69676fa9940b7a0dc9c6dcd6cbda6 schema:name Springer Nature - SN SciGraph project
    47 rdf:type schema:Organization
    48 Nbbe0ee6707bc46a0bf978cfe9c01bfc5 schema:name nsd_ids_id
    49 schema:value 445998
    50 rdf:type schema:PropertyValue
    51 Nbc89881c3b5f48e99d9644daacdbc41a schema:name lccn_id
    52 schema:value 2004229332
    53 rdf:type schema:PropertyValue
    54 Nbd19a5eb91d9447fbc2700993a298bff schema:name dimensions_id
    55 schema:value 135929
    56 rdf:type schema:PropertyValue
    57 Nce2df2514bbb4cbe8310d5e6414b422a rdf:first N886d3fc37e8149898b746a0ba3085dc1
    58 rdf:rest rdf:nil
    59 Nda07d8a50d554aa099c3717f1a32645b schema:name era_ids_id
    60 schema:value 5325
    61 rdf:type schema:PropertyValue
     




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


    ...