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Generating of land suitability index for wheat with hybrid system aproach using AHP and GIS

Computers and Electronics in Agriculture · Aralık 2019

Özet
Today, crop models have been developed to products of strategic importance for precision agriculture management in the countries. The objective of this study was to generate the wheat suitability index (WSI) by using hybrid system that is including qualitative and quantitative reasons such as expert and sciencific knowledge weighted with analytic hierarchy process (AHP). This aproach was integrated into the GIS based on Linear Combination Teqnique in the study. For this purpose, the study was conducted in the field of wheat cultivation in Soğulca Basin with an area of 68.04 km2 located on Central Anatolia Region of Turkey. We have selected 10 criteria as physical, chemical and topographical that affects wheat cultivation in the basin which has been divided into 47 land units according to thematic soil map. With the WSI model, 32.05% of the study area was classified as highly and moderately suitable whereas, 67.95% of the total study area has marginally and not suitable properties for wheat cultivation. According to results, the most effective factors on the last score values for WSI were found soil depth, texture and slope indicators. The score values of the WSI were compared with 5 years (2013–2017) yields and NDVI values for testing of the model and it has been determined that land classification for wheat has been done with high accuracy for yield r2 = 0.83% and for NDVI r2 = 0.78%. The results of the study showed that the WSI was found as convenient model in semi-arid climate condition. However, we suggest that the WSI model should also be tested in similar climatic conditions and in different soil types in order to be available as a general - pass index. In addition, using AHP with GIS capabilities, we have high capacity to the integration of heterogeneous data for determination and classification of suitability in the agriculture areas.
111 atıf Aralık 2019 DOI
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YÖKSİS Kayıtları
Generating of land suitability index for wheat with hybrid system aproach using AHP and GIS
COMPUTERS AND ELECTRONICS IN AGRICULTURE · 2019 SCI-Expanded
Prof. Dr. MERT DEDEOĞLU →
YÖKSİS Kayıtları — ISSN Eşleşmesi
Bu dergide (ISSN eşleşmesi) kurumun 12 kaydı bulundu.
A Spatial Decision Support System design for land reallocation A case study in Turkey
2013 ISSN: 0168-1699 SCI-Expanded
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Determination of body measurements on the Holstein cows using digital image analysis and estimation of live weight with regression analysis
2011 ISSN: 01681699 SCI-Expanded
Prof. Dr. ŞAKİR TAŞDEMİR →
A Spatial Decision Support System design for land reallocation: A case study in Turkey
2013 ISSN: 01681699 SCI-Expanded
Öğr. Gör. OĞUZKAĞAN AKÇAKAYA →
Towards a real-time sorting system: Identification of vitreous durum wheat kernels using ANN based on their morphological, colour, wavelet and gaborlet features
2019 ISSN: 0168-1699 SCI-Expanded
Prof. Dr. İSMAİL SARITAŞ →
Generating of land suitability index for wheat with hybrid system aproach using AHP and GIS
2019 ISSN: 0168-1699 SCI-Expanded
Prof. Dr. MERT DEDEOĞLU →
Multiclass Classification of Dry Beans Using Computer Vision and Machine Learning Techniques
2020 ISSN: 0168-1699 SCI-Expanded Q1
Doç. Dr. MURAT KÖKLÜ →
Classification of Rice Varieties with Deep Learning Methods
2021 ISSN: 0168-1699 SCI-Expanded Q1
Dr. Öğr. Üyesi İLKAY ÇINAR →
Dry bean cultivars classification using deep cnn features and salp swarm algorithm based extreme learning machine
2023 ISSN: 0168-1699 SCI-Expanded Q1
Öğr. Gör. RAMAZAN KURŞUN →
Dry Bean Cultivars Classification Using Deep CNN Features and Salp Swarm Algorithm Based Extreme Learning Machine
2023 ISSN: 0168-1699 SCI-Expanded Q1
Doç. Dr. MURAT KÖKLÜ →
Classification of Rice Varieties with Deep Learning Methods
2021 ISSN: 0168-1699 SCI-Expanded Q1
Doç. Dr. MURAT KÖKLÜ →
Towards a real-time sorting system: Identification of vitreous durum wheat kernels using ANN based on their morphological, colour, wavelet and gaborlet features
2019 ISSN: 0168-1699 SCI-Expanded Q1
Dr. Öğr. Üyesi ESRA KAYA ERDOĞAN →
Dry bean cultivars classification using deep cnn features and salp swarm algorithm based extreme learning machine
2023 ISSN: 0168-1699 SCI-Expanded Q1
Doç. Dr. İLKER ALİ ÖZKAN →

Makale Bilgileri

Toplam Atıf 111 atıf · Scopus
ISSN01681699
Yayın TarihiAralık 2019
Cilt / Sayfa167

Kurumlar

Ondokuz Mayis Üniversitesi
Samsun Turkey
Selçuk Üniversitesi
Selçuklu Turkey

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Bu makaleye, sistemimizdeki Scopus veritabanında bulunan 0 makale atıf yapmıştır. Scopus genel atıf sayısı: 111.

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Scimago Dergi (ISSN Eşleşmesi)
Computers and Electronics in Agriculture
Q1
SJR Skoru2,165
H-Index209
YayıncıElsevier B.V.
ÜlkeNetherlands
Agronomy and Crop Science (Q1)
Animal Science and Zoology (Q1)
Computer Science Applications (Q1)
Forestry (Q1)
Horticulture (Q1)
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111
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