Conference
Προβολή σε EDS

Leaf detection using Python.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Leaf detection using Python.
Συγγραφείς: Addenki, Krishnaveni1 (AUTHOR) krishnaveni.nhce@gmail.com, Dan, Rama Banshidhar1 (AUTHOR) ramadan.nhce@gmail.com, Atharvi, Ravi1 (AUTHOR) armahindrakar2020@gmail.com, Tripathi, Astha1 (AUTHOR) asthatripathi7205@gmail.com
Πηγή: AIP Conference Proceedings. 2026, Vol. 3359 Issue 1, p1-6. 6p.
Θεματικοί όροι: *Python programming language, *Image processing, *Precision farming, *Computer vision, *Agricultural technology, *Leaf spots, *Machine learning
Περίληψη: The advancement of technology in agriculture has paved the way for innovative approaches to crop management. One such approach is the detection and analysis of leaves using Python, which leverages machine learning and computer vision techniques. This process involves capturing images of leaves and processing them through algorithms that can identify and classify various features, such as shape, size, and color. By using libraries such as OpenCV for image processing and TensorFlow or PyTorch for model training, Python provides a robust framework for accurate leaf detection. This technology has substantial implications for precision agriculture, enabling early disease detection, monitoring plant health, and optimizing resource use. Consequently, the incorporation of Python-based leaf detection systems can enhance agricultural productivity and sustainability. [ABSTRACT FROM AUTHOR]
Βάση Δεδομένων: Academic Search Index
FullText Text:
  Availability: 0
Header DbId: asx
DbLabel: Academic Search Index
An: 197256438
RelevancyScore: 1467
AccessLevel: 6
PubType: Conference
PubTypeId: conference
PreciseRelevancyScore: 1467.42810058594
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Leaf detection using Python.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Addenki%2C+Krishnaveni%22">Addenki, Krishnaveni</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> krishnaveni.nhce@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Dan%2C+Rama+Banshidhar%22">Dan, Rama Banshidhar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ramadan.nhce@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Atharvi%2C+Ravi%22">Atharvi, Ravi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> armahindrakar2020@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Tripathi%2C+Astha%22">Tripathi, Astha</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> asthatripathi7205@gmail.com</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22AIP+Conference+Proceedings%22">AIP Conference Proceedings</searchLink>. 2026, Vol. 3359 Issue 1, p1-6. 6p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Python+programming+language%22">Python programming language</searchLink><br />*<searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br />*<searchLink fieldCode="DE" term="%22Precision+farming%22">Precision farming</searchLink><br />*<searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br />*<searchLink fieldCode="DE" term="%22Agricultural+technology%22">Agricultural technology</searchLink><br />*<searchLink fieldCode="DE" term="%22Leaf+spots%22">Leaf spots</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The advancement of technology in agriculture has paved the way for innovative approaches to crop management. One such approach is the detection and analysis of leaves using Python, which leverages machine learning and computer vision techniques. This process involves capturing images of leaves and processing them through algorithms that can identify and classify various features, such as shape, size, and color. By using libraries such as OpenCV for image processing and TensorFlow or PyTorch for model training, Python provides a robust framework for accurate leaf detection. This technology has substantial implications for precision agriculture, enabling early disease detection, monitoring plant health, and optimizing resource use. Consequently, the incorporation of Python-based leaf detection systems can enhance agricultural productivity and sustainability. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asx&AN=197256438
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1063/5.0306852
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 6
        StartPage: 1
    Subjects:
      – SubjectFull: Python programming language
        Type: general
      – SubjectFull: Image processing
        Type: general
      – SubjectFull: Precision farming
        Type: general
      – SubjectFull: Computer vision
        Type: general
      – SubjectFull: Agricultural technology
        Type: general
      – SubjectFull: Leaf spots
        Type: general
      – SubjectFull: Machine learning
        Type: general
    Titles:
      – TitleFull: Leaf detection using Python.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Addenki, Krishnaveni
      – PersonEntity:
          Name:
            NameFull: Dan, Rama Banshidhar
      – PersonEntity:
          Name:
            NameFull: Atharvi, Ravi
      – PersonEntity:
          Name:
            NameFull: Tripathi, Astha
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 22
              M: 09
              Text: 2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 0094243X
          Numbering:
            – Type: volume
              Value: 3359
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: AIP Conference Proceedings
              Type: main
ResultId 1