rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Computer vision-based agriculture engineering
  name:
    name part: Zhongzhi, Han
    name part: 1981-
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: EBSCOhost
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: nyu
    date issued: 2020
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  table of contents: Machine generated contents note: ch. 1 Detecting Anatoxin in Agricultural Products by Hyperspectral Imaging: A Review -- 1.1.Introduction -- 1.2.Main Detecting Methods -- 1.2.1.Hyperspectral Imaging (HSI) -- 1.2.2.Near-Infrared Spectroscopy (NIRS) -- 1.2.3.Flow Chart Showing Typical Steps -- 1.3.Detection of Anatoxin in Agricultural Products -- 1.3.1.Corn or Maize -- 1.3.1.1.Detection of Anatoxin by Hyperspectral Images -- 1.3.1.2.Detection of Anatoxin by Other Methods -- 1.3.1.3.Detect Other Fungi -- 1.3.2.Cereals, Nuts, and Others -- 1.3.2.1.Wheat, Barley, and Rice -- 1.3.2.2.Pistachio Nuts, Hazelnuts, Brazil Nuts, and Peanuts -- 1.3.2.3.Chili Pepper -- 1.4.Limitation and Future Trends -- 1.4.1.Limitation -- 1.4.2.Future Trends -- 1.5.Conclusions -- References -- ch. 2 Anatoxin Detection by Fluorescence Index and Narrowband Spectra Based on Hyperspectral Imaging -- 2.1.Introduction -- 2.2.Experiment Materials -- 2.2.1.Sample Preparation and Image Acquisition -- 2.2.2.Illumination Compensation and Kernel Segmentation -- 2.3.Data Processing and Result Analysis -- 2.3.1.Fluorescence Index -- 2.3.2.Recognition and Regression -- 2.3.3.Narrowband Spectra -- 2.4.Discussion -- 2.5.Conclusions -- References -- ch. 3 Application-Driven Key Wavelength Mining Method for Anatoxin Detection Using Hyperspectral Data -- 3.1.Introduction -- 3.2.Materials -- 3.2.1.Experiment Materials -- 3.2.2.System Integration -- 3.3.Methods -- 3.3.1.Data Preprocessing -- 3.3.2.Recognition Methods -- 3.4.Results -- 3.4.1.Hyperspectral Wave by ASD -- 3.4.2.Multispectral Images by Liquid Crystal Tunable Filter (HTLF) -- 3.4.3.Hyperspectral Images by GSM -- 3.5.Discussion -- 3.5.1.Key Wavelengths Selected by Weighted Voting -- 3.5.2.Sorter Design -- 3.6.Conclusion -- References -- ch. 4 Deep Learning-Based Anatoxin Detection of Hyperspectral Data -- 4.1.Introduction -- 4.2.Materials and Methods -- 4.2.1.Peanut Sample Preparation -- 4.2.2.Hyperspectral Imaging System and Image Acquisition -- 4.2.3.Hy…
  table of contents: Note continued: ch. 20 Study on Vegetable Seed Electrophoresis Image Classification Method -- 20.1.Introduction -- 20.2.Material and Method -- 20.2.1.Experimental Materials -- 20.2.2.Method -- 20.3.Results and Analysis -- 20.3.1.Recognition of Corp -- 20.3.2.Cluster Analysis -- 20.4.Discussions -- 20.5.Conclusions -- References -- ch. 21 Identifying the Change Process of a Fresh Pepper by Transfer Learning -- 21.1.Introduction -- 21.2.Materials and Method -- 21.2.1.Image Characteristics -- 21.2.2.Workflow Diagram -- 21.2.3.Convolutional Neural Network -- 21.2.4.Transfer Learning -- 21.3.The Performance of the Model -- 21.4.Computer vs Humans -- 21.5.Expanding Application -- 21.6.Discussion -- 21.7.Conclusions and Future Work -- References -- ch. 22 Identifying the Change Process of Fresh Banana by Transfer Learning -- 22.1.Introduction -- 22.2.Materials and Method -- 22.2.1.Image Dataset -- 22.2.2.Convolutional Neural Network -- 22.2.3.Transfer Learning -- 22.2.4.Experimental Setup -- 22.3.The Performance of Model -- 22.3.1.Experimental Result -- 22.3.2.Computer vs Humans -- 22.3.3.Expanding Application -- 22.4.Discussion -- 22.5.Conclusions -- References -- ch. 23 Pest Recognition Using Transfer Learning -- 23.1.Introduction -- 23.2.Materials and Methods -- 23.2.1.Materials -- 23.2.2.Introduce of the Model -- 23.3.Result and Analysis -- 23.3.1.Pests Recognition Result by Transfer Learning Model -- 23.3.2.Comparison of the Model with Traditional Methods -- 23.3.3.Comparison of the Model with Human Expert -- 23.3.4.Universal of the Transfer Learning Model -- 23.4.Discussion -- 23.4.1.Image Numbers and Image Capture Environment -- 23.4.2.Image Background and Segmentation -- 23.4.3.Similar Outline Disturb -- 23.5.Conclusion -- References -- ch. 24 Using Deep Learning for Image-Based Plant Disease Detection -- 24.1.Introduction -- 24.2.Materials and Methods -- 24.2.1.Dataset -- 24.2.2.Introduced Model -- 24.2.3.Experiment Environment -- 24.2.4.Experiment Result -- 24.3.…
  note: Han Zhongzhi.
  note: Includes bibliographical references and index.
  subject:
    topic: Agricultural engineering
    topic: Technological innovations
  subject:
    topic: Agriculture
    topic: Remote sensing
  subject:
    topic: Quality control
    topic: Optical methods
  subject:
    topic: Génie rural
    topic: Innovations
  subject:
    topic: Agriculture
    topic: Télédétection
  subject:
    topic: Qualité
    topic: Contrôle
    topic: Méthodes optiques
  subject:
    topic: SCIENCE
    topic: Life Sciences
    topic: Botany
  subject:
    topic: TECHNOLOGY
    topic: Agriculture
    topic: General
  subject:
    topic: TECHNOLOGY
    topic: Remote Sensing
  subject:
    topic: Agriculture
    topic: Remote sensing
  subject:
    topic: Quality control
    topic: Optical methods
  classification: S675 .Z46 2020
  classification: 631.5/233
  related item:
    title info:
      title: Computer vision-based agriculture engineering
    name:
      name part: Zhongzhi, Han, 1981-
    origin info:
      publisher: New York, NY : CRC Press, Taylor & Francis Group, [2020]
    identifier: (DLC) 2019025856
  identifier: 9780429289460
  identifier: 0429289464
  identifier: 9781000691955
  identifier: 1000691950
  identifier: 9781000691788
  identifier: 1000691780
  identifier: 9781000691610
  identifier: 1000691616
  identifier: 9780367254308
  identifier: 0367254301
  identifier: 2019025857
  identifier: 9780429289460 Taylor & Francis
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: DLC
    record creation date: 190619
    record change date: 20240909140345.9
    record identifier: in00005613657
    language of cataloging:
      language term: eng