View in EDS

Plant Disease Recognition: A Large-Scale Benchmark Dataset and a Visual Region and Loss Reweighting Approach.

Saved in:
Bibliographic Details
Title: Plant Disease Recognition: A Large-Scale Benchmark Dataset and a Visual Region and Loss Reweighting Approach.
Authors: Liu, Xinda1 (AUTHOR) liuxinda@buaa.edu.cn, Min, Weiqing2 (AUTHOR) minweiqing@ict.ac.cn, Mei, Shuhuan3 (AUTHOR) long8622416@163.com, Wang, Lili1 (AUTHOR) wanglily@buaa.edu.cn, Jiang, Shuqiang2 (AUTHOR) sqjiang@ict.ac.cn
Source: IEEE Transactions on Image Processing. 2021, Vol. 30, p2003-2015. 13p.
Subjects: Image processing, Plant diseases, Image recognition (Computer vision), Agricultural productivity, Recognition (Psychology), Hebbian memory, Feature extraction
Abstract: Plant disease diagnosis is very critical for agriculture due to its importance for increasing crop production. Recent advances in image processing offer us a new way to solve this issue via visual plant disease analysis. However, there are few works in this area, not to mention systematic researches. In this paper, we systematically investigate the problem of visual plant disease recognition for plant disease diagnosis. Compared with other types of images, plant disease images generally exhibit randomly distributed lesions, diverse symptoms and complex backgrounds, and thus are hard to capture discriminative information. To facilitate the plant disease recognition research, we construct a new large-scale plant disease dataset with 271 plant disease categories and 220,592 images. Based on this dataset, we tackle plant disease recognition via reweighting both visual regions and loss to emphasize diseased parts. We first compute the weights of all the divided patches from each image based on the cluster distribution of these patches to indicate the discriminative level of each patch. Then we allocate the weight to each loss for each patch-label pair during weakly-supervised training to enable discriminative disease part learning. We finally extract patch features from the network trained with loss reweighting, and utilize the LSTM network to encode the weighed patch feature sequence into a comprehensive feature representation. Extensive evaluations on this dataset and another public dataset demonstrate the advantage of the proposed method. We expect this research will further the agenda of plant disease recognition in the community of image processing. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Image Processing is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 170077641
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Plant Disease Recognition: A Large-Scale Benchmark Dataset and a Visual Region and Loss Reweighting Approach.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Xinda%22">Liu, Xinda</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> liuxinda@buaa.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Min%2C+Weiqing%22">Min, Weiqing</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> minweiqing@ict.ac.cn</i><br /><searchLink fieldCode="AR" term="%22Mei%2C+Shuhuan%22">Mei, Shuhuan</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> long8622416@163.com</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Lili%22">Wang, Lili</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wanglily@buaa.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Jiang%2C+Shuqiang%22">Jiang, Shuqiang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> sqjiang@ict.ac.cn</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Image+Processing%22">IEEE Transactions on Image Processing</searchLink>. 2021, Vol. 30, p2003-2015. 13p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Plant+diseases%22">Plant diseases</searchLink><br /><searchLink fieldCode="DE" term="%22Image+recognition+%28Computer+vision%29%22">Image recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Agricultural+productivity%22">Agricultural productivity</searchLink><br /><searchLink fieldCode="DE" term="%22Recognition+%28Psychology%29%22">Recognition (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Hebbian+memory%22">Hebbian memory</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Plant disease diagnosis is very critical for agriculture due to its importance for increasing crop production. Recent advances in image processing offer us a new way to solve this issue via visual plant disease analysis. However, there are few works in this area, not to mention systematic researches. In this paper, we systematically investigate the problem of visual plant disease recognition for plant disease diagnosis. Compared with other types of images, plant disease images generally exhibit randomly distributed lesions, diverse symptoms and complex backgrounds, and thus are hard to capture discriminative information. To facilitate the plant disease recognition research, we construct a new large-scale plant disease dataset with 271 plant disease categories and 220,592 images. Based on this dataset, we tackle plant disease recognition via reweighting both visual regions and loss to emphasize diseased parts. We first compute the weights of all the divided patches from each image based on the cluster distribution of these patches to indicate the discriminative level of each patch. Then we allocate the weight to each loss for each patch-label pair during weakly-supervised training to enable discriminative disease part learning. We finally extract patch features from the network trained with loss reweighting, and utilize the LSTM network to encode the weighed patch feature sequence into a comprehensive feature representation. Extensive evaluations on this dataset and another public dataset demonstrate the advantage of the proposed method. We expect this research will further the agenda of plant disease recognition in the community of image processing. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of IEEE Transactions on Image Processing is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=170077641
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1109/TIP.2021.3049334
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 2003
    Subjects:
      – SubjectFull: Image processing
        Type: general
      – SubjectFull: Plant diseases
        Type: general
      – SubjectFull: Image recognition (Computer vision)
        Type: general
      – SubjectFull: Agricultural productivity
        Type: general
      – SubjectFull: Recognition (Psychology)
        Type: general
      – SubjectFull: Hebbian memory
        Type: general
      – SubjectFull: Feature extraction
        Type: general
    Titles:
      – TitleFull: Plant Disease Recognition: A Large-Scale Benchmark Dataset and a Visual Region and Loss Reweighting Approach.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Liu, Xinda
      – PersonEntity:
          Name:
            NameFull: Min, Weiqing
      – PersonEntity:
          Name:
            NameFull: Mei, Shuhuan
      – PersonEntity:
          Name:
            NameFull: Wang, Lili
      – PersonEntity:
          Name:
            NameFull: Jiang, Shuqiang
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 07
              Text: 2021
              Type: published
              Y: 2021
          Identifiers:
            – Type: issn-print
              Value: 10577149
          Numbering:
            – Type: volume
              Value: 30
          Titles:
            – TitleFull: IEEE Transactions on Image Processing
              Type: main
ResultId 1