An approximation for a relative crop yield estimate from field images using deep learning

Hulya Yalcin*

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: ???type-name???Konferans katkısıbilirkişi

26 Atıf (Scopus)

Özet

Smart farming and precision agriculture are becoming increasingly important to cope with challenges due to the growth of world population. Accurate crop yield prediction is an indispensable part of modern agricultural technologies to ensure food security and sustainability encountered in agricultural production. Since environmental conditions highly affect a plant's growth, accurate estimation of crop yield can provide a lot of information that can be used for maintaining the quality of crop production. In this paper, a deep learning architecture is utilized to estimate crop yield in field images. The plant images are captured every half an hour by cameras mounted on the ground agricultural stations. We utilize intermediate outputs of deep learning architectures to develop a measure for an approximate estimate crop yield. This estimate represents a relative measure for crop yield estimate, relative to the high crop yield estimates in agricultural parcels that were used while training the deep learning architecture. We experimented our approach on sunflower image sequences collected from four different parcels and obtained promising results.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığı2019 8th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2019
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9781728121161
DOI'lar
Yayın durumuYayınlandı - Tem 2019
Etkinlik8th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2019 - Istanbul, Turkey
Süre: 16 Tem 201919 Tem 2019

Yayın serisi

Adı2019 8th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2019

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???event.eventtypes.event.conference???8th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2019
Ülke/BölgeTurkey
ŞehirIstanbul
Periyot16/07/1919/07/19

Bibliyografik not

Publisher Copyright:
© 2019 IEEE.

Finansman

ACKNOWLEDGMENT Data for this work is provided by Istanbul Technical University TARBIL Agro-informatics Research Center. This work is funded by the grants of Istanbul Technical University Scientific Research Fund (project # 34912), Turkish Ministry of Food, Agriculture and Livestock, and ITU TARBIL Agro-informatics Research Fund (project # 2012A020130) and TÜB TAK fund (project # 118E057). Data for this work is provided by Istanbul Technical University TARBIL Agro-informatics Research Center. This work is funded by the grants of Istanbul Technical University Scientific Research Fund (project # 34912), Turkish Ministry of Food, Agriculture and Livestock, and ITU TARBIL Agroinformatics Research Fund (project # 2012A020130) and TUBITAK fund (project # 118E057).

FinansörlerFinansör numarası
Istanbul Technical University Scientific Research Fund34912
Istanbul Technical University TARBIL Agro-informatics Research Center
TUBITAK fund
TÜB TAK fund118E057
Ministry for Food, Agriculture, Forestry and Fisheries2012A020130

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