Öǧrenme transferi ile bitki gelişiminin görsel analizi

Hulya Yalcin*

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Özet

Attention for monitoring growth of plants has been escalating in recent years, due to concerns on climate change and its impacts on the ecosystem. Understanding the growth characteristics of plants is vital for management of resources and optimization of crop yield in agricultural sector as well. Each plant population exhibits varying seasonal growth and reproduction manner with respect to the local environmental parameters. Using computational power of advancing technology is becoming unavoidable for the optimal usage of resources and human labor. In this paper, growth stages of plants are analyzed using recently advancing technologies of transfer learning. Deep learning is utilized for visual analysis of a variety of plants at different growing stages. The performance of a particular deep learning architecture is investigated for phenology recognition of agricultural plants at roughly three phenological stages, namely early, middle and late phenological stages.

Tercüme edilen katkı başlığıVisual analysis of plant growth using transfer learning
Orijinal dilTürkçe
Ana bilgisayar yayını başlığıSIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9781665436496
DOI'lar
Yayın durumuYayınlandı - 9 Haz 2021
Etkinlik29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021 - Virtual, Istanbul, Turkey
Süre: 9 Haz 202111 Haz 2021

Yayın serisi

AdıSIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings

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???event.eventtypes.event.conference???29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021
Ülke/BölgeTurkey
ŞehirVirtual, Istanbul
Periyot9/06/2111/06/21

Bibliyografik not

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Computer vision
  • Deep learning
  • Plant phenology
  • Precision agriculture

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