Tarla Görüntülerinden Ürün Türü Tahmini

Sinasi Durmus; Ulug Bayazit

doi:10.1109/SIU.2017.7960312

Tarla Görüntülerinden Ürün Türü Tahmini

Sinasi Durmus^*, Ulug Bayazit

^*Bu çalışma için yazışmadan sorumlu yazar

Bilgisayar Mühendisliği Bölümü

Istanbul Technical University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

3 Atıf (Scopus)

Özet

This paper proposes methods to classify the plants using images taken from agricultural lands. Wheat, maize and lentil images are used. Texture features of agricultural land images are obtained using Gray Level Co-occurrence Matrix (GLCM) and Laws' Texture Energy Measures which are two of texture analysis methods. The texture features vectors which are generated with these two methods are classified with different classifiers. Agricultural land images are separated to three different classes using k-Nearest Neighbors (k-NN) algorithm, Support Vector Machines (SVM) and Naive Bayes Classifiers. It is understand that Gray Level Co-occurrence Matrix and Laws' Texture Energy Measures are sensitive to field images. Classification of Laws' Texture Energy Measures data yields 100% performance in k-Nearest Neighbors and Support Vector Machines methods. Laws' Texture Energy Measures yield better performance than Gray Level Co-occurrence Matrix.

Tercüme edilen katkı başlığı	Plant species estimation from field images
Orijinal dil	Türkçe
Ana bilgisayar yayını başlığı	2017 25th Signal Processing and Communications Applications Conference, SIU 2017
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)	9781509064946
DOI'lar	https://doi.org/10.1109/SIU.2017.7960312
Yayın durumu	Yayınlandı - 27 Haz 2017
Etkinlik	25th Signal Processing and Communications Applications Conference, SIU 2017 - Antalya, Turkey Süre: 15 May 2017 → 18 May 2017

Yayın serisi

Adı	2017 25th Signal Processing and Communications Applications Conference, SIU 2017

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	25th Signal Processing and Communications Applications Conference, SIU 2017
Ülke/Bölge	Turkey
Şehir	Antalya
Periyot	15/05/17 → 18/05/17

Bibliyografik not

Publisher Copyright:
© 2017 IEEE.

Keywords

classification
field
plant
texture

Belgeye Erişim

10.1109/SIU.2017.7960312

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

@inproceedings{cc198308fe6542bf88deff30610b667b,

title = "Tarla G{\"o}r{\"u}nt{\"u}lerinden {\"U}r{\"u}n T{\"u}r{\"u} Tahmini",

abstract = "This paper proposes methods to classify the plants using images taken from agricultural lands. Wheat, maize and lentil images are used. Texture features of agricultural land images are obtained using Gray Level Co-occurrence Matrix (GLCM) and Laws' Texture Energy Measures which are two of texture analysis methods. The texture features vectors which are generated with these two methods are classified with different classifiers. Agricultural land images are separated to three different classes using k-Nearest Neighbors (k-NN) algorithm, Support Vector Machines (SVM) and Naive Bayes Classifiers. It is understand that Gray Level Co-occurrence Matrix and Laws' Texture Energy Measures are sensitive to field images. Classification of Laws' Texture Energy Measures data yields 100% performance in k-Nearest Neighbors and Support Vector Machines methods. Laws' Texture Energy Measures yield better performance than Gray Level Co-occurrence Matrix.",

keywords = "classification, field, plant, texture",

author = "Sinasi Durmus and Ulug Bayazit",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 25th Signal Processing and Communications Applications Conference, SIU 2017 ; Conference date: 15-05-2017 Through 18-05-2017",

year = "2017",

month = jun,

day = "27",

doi = "10.1109/SIU.2017.7960312",

language = "T{\"u}rk{\c c}e",

series = "2017 25th Signal Processing and Communications Applications Conference, SIU 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2017 25th Signal Processing and Communications Applications Conference, SIU 2017",

address = "United States",

}

Durmus, S & Bayazit, U 2017, Tarla Görüntülerinden Ürün Türü Tahmini. in 2017 25th Signal Processing and Communications Applications Conference, SIU 2017., 7960312, 2017 25th Signal Processing and Communications Applications Conference, SIU 2017, Institute of Electrical and Electronics Engineers Inc., 25th Signal Processing and Communications Applications Conference, SIU 2017, Antalya, Turkey, 15/05/17. https://doi.org/10.1109/SIU.2017.7960312

Tarla Görüntülerinden Ürün Türü Tahmini. / Durmus, Sinasi; Bayazit, Ulug.
2017 25th Signal Processing and Communications Applications Conference, SIU 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 7960312 (2017 25th Signal Processing and Communications Applications Conference, SIU 2017).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Tarla Görüntülerinden Ürün Türü Tahmini

AU - Durmus, Sinasi

AU - Bayazit, Ulug

PY - 2017/6/27

Y1 - 2017/6/27

N2 - This paper proposes methods to classify the plants using images taken from agricultural lands. Wheat, maize and lentil images are used. Texture features of agricultural land images are obtained using Gray Level Co-occurrence Matrix (GLCM) and Laws' Texture Energy Measures which are two of texture analysis methods. The texture features vectors which are generated with these two methods are classified with different classifiers. Agricultural land images are separated to three different classes using k-Nearest Neighbors (k-NN) algorithm, Support Vector Machines (SVM) and Naive Bayes Classifiers. It is understand that Gray Level Co-occurrence Matrix and Laws' Texture Energy Measures are sensitive to field images. Classification of Laws' Texture Energy Measures data yields 100% performance in k-Nearest Neighbors and Support Vector Machines methods. Laws' Texture Energy Measures yield better performance than Gray Level Co-occurrence Matrix.

AB - This paper proposes methods to classify the plants using images taken from agricultural lands. Wheat, maize and lentil images are used. Texture features of agricultural land images are obtained using Gray Level Co-occurrence Matrix (GLCM) and Laws' Texture Energy Measures which are two of texture analysis methods. The texture features vectors which are generated with these two methods are classified with different classifiers. Agricultural land images are separated to three different classes using k-Nearest Neighbors (k-NN) algorithm, Support Vector Machines (SVM) and Naive Bayes Classifiers. It is understand that Gray Level Co-occurrence Matrix and Laws' Texture Energy Measures are sensitive to field images. Classification of Laws' Texture Energy Measures data yields 100% performance in k-Nearest Neighbors and Support Vector Machines methods. Laws' Texture Energy Measures yield better performance than Gray Level Co-occurrence Matrix.

KW - classification

KW - field

KW - plant

KW - texture

UR - http://www.scopus.com/inward/record.url?scp=85026295348&partnerID=8YFLogxK

U2 - 10.1109/SIU.2017.7960312

DO - 10.1109/SIU.2017.7960312

M3 - Konferans katkısı

AN - SCOPUS:85026295348

T3 - 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

BT - 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 25th Signal Processing and Communications Applications Conference, SIU 2017

Y2 - 15 May 2017 through 18 May 2017

ER -

Tarla Görüntülerinden Ürün Türü Tahmini

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Keywords

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap