Tarla Görüntülerinden Ürün Türü Tahmini

Sinasi Durmus; Ulug Bayazit

doi:10.1109/SIU.2017.7960312

Tarla Görüntülerinden Ürün Türü Tahmini

Translated title of the contribution: Plant species estimation from field images

Sinasi Durmus^*, Ulug Bayazit

^*Corresponding author for this work

Department of Computer Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

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.

Translated title of the contribution	Plant species estimation from field images
Original language	Turkish
Title of host publication	2017 25th Signal Processing and Communications Applications Conference, SIU 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509064946
DOIs	https://doi.org/10.1109/SIU.2017.7960312
Publication status	Published - 27 Jun 2017
Event	25th Signal Processing and Communications Applications Conference, SIU 2017 - Antalya, Turkey Duration: 15 May 2017 → 18 May 2017

Publication series

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

Conference

Conference	25th Signal Processing and Communications Applications Conference, SIU 2017
Country/Territory	Turkey
City	Antalya
Period	15/05/17 → 18/05/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Access to Document

10.1109/SIU.2017.7960312

Cite this

@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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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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

Abstract

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