Investigation of Deep Learning Approaches for Identification of Important Wheat Pests in Central Anatolia

Tolga Hayıt; Sadık Eren Köse

Makale detayları

Investigation of Deep Learning Approaches for Identification of Important Wheat Pests in Central Anatolia

Yazarlar:

Tolga Hayıt | Sadık Eren Köse

Yayıncı:

Hitit University

Ana Başlıklar:

Engineering

Dergi:

Hittite Journal of Science and Engineering Vol.10 Issue 3, pp.249-257

Belge Tipi:

Makale

Yayın Tarihi:

9/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001300058

pISSN:

2148-4171

Anahtar kelimeler:

Özet:

 Artificial intelligence-based systems play a crucial role in Integrated Pest Management studies. It is important to develop and support such systems for controlling wheat pests, which cause significant losses in wheat production which is strategic importance, particularly in Turkey. This study employed various pre-trained deep learning approaches to identify key wheat pests in the Central Anatolia Region, namely Aelia spp., Anisoplia spp., Eurygaster spp., Pachytychius hordei, and Zabrus spp. The models classification success was determined using open and original datasets. Among the models, the ResNet-18 model outperformed others, achieving a classification success rate of 99. Furthermore, each model was tested with original images collected during field studies to assess their effectiveness. The results demonstrate that pre-trained deep learning models can be utilized for the identification of important wheat pests in Central Anatolia as part of Integrated Pest Management.

 Artificial intelligence-based systems play a crucial role in Integrated Pest Management studies. It is important to develop and support such systems for controlling wheat pests, which cause significant losses in wheat production which is strategic importance, particularly in Turkey. This study employed various pre-trained deep learning approaches to identify key wheat pests in the Central Anatolia Region, namely Aelia spp., Anisoplia spp., Eurygaster spp., Pachytychius hordei, and Zabrus spp. The models classification success was determined using open and original datasets. Among the models, the ResNet-18 model outperformed others, achieving a classification success rate of 99. Furthermore, each model was tested with original images collected during field studies to assess their effectiveness. The results demonstrate that pre-trained deep learning models can be utilized for the identification of important wheat pests in Central Anatolia as part of Integrated Pest Management.

 Artificial intelligence-based systems play a crucial role in Integrated Pest Management studies. It is important to develop and support such systems for controlling wheat pests, which cause significant losses in wheat production which is strategic importance, particularly in Turkey. This study employed various pre-trained deep learning approaches to identify key wheat pests in the Central Anatolia Region, namely Aelia spp., Anisoplia spp., Eurygaster spp., Pachytychius hordei, and Zabrus spp. The models classification success was determined using open and original datasets. Among the models, the ResNet-18 model outperformed others, achieving a classification success rate of 99. Furthermore, each model was tested with original images collected during field studies to assess their effectiveness. The results demonstrate that pre-trained deep learning models can be utilized for the identification of important wheat pests in Central Anatolia as part of Integrated Pest Management.

 Artificial intelligence-based systems play a crucial role in Integrated Pest Management studies. It is important to develop and support such systems for controlling wheat pests, which cause significant losses in wheat production which is strategic importance, particularly in Turkey. This study employed various pre-trained deep learning approaches to identify key wheat pests in the Central Anatolia Region, namely Aelia spp., Anisoplia spp., Eurygaster spp., Pachytychius hordei, and Zabrus spp. The models classification success was determined using open and original datasets. Among the models, the ResNet-18 model outperformed others, achieving a classification success rate of 99. Furthermore, each model was tested with original images collected during field studies to assess their effectiveness. The results demonstrate that pre-trained deep learning models can be utilized for the identification of important wheat pests in Central Anatolia as part of Integrated Pest Management.

Makale detayları

Investigation of Deep Learning Approaches for Identification of Important Wheat Pests in Central Anatolia

Yazarlar:

Tolga Hayıt | Sadık Eren Köse

Yayıncı:

Hitit University

Ana Başlıklar:

Engineering

Dergi:

Hittite Journal of Science and Engineering Vol.10 Issue 3, pp.249-257

Belge Tipi:

Makale

Yayın Tarihi:

9/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001300058

pISSN:

2148-4171

Important Wheat Pests

Insect Classification

Deep Learning

CNN

Transfer Learning

ResNet-18

Sayı

Makale detayları

Investigation of Deep Learning Approaches for Identification of Important Wheat Pests in Central Anatolia

Yazarlar: Tolga Hayıt | Sadık Eren Köse

Yayıncı: Hitit University

Ana Başlıklar: Engineering

Dergi: Hittite Journal of Science and Engineering Vol.10 Issue 3, pp.249-257

Belge Tipi: Makale

Yayın Tarihi: 9/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001300058

pISSN: 2148-4171

Important Wheat Pests

Insect Classification

Deep Learning

CNN

Transfer Learning

ResNet-18

Sayı

Yazarlar:

Tolga Hayıt | Sadık Eren Köse

Yayıncı:

Hitit University

Ana Başlıklar:

Engineering

Dergi:

Hittite Journal of Science and Engineering Vol.10 Issue 3, pp.249-257

Belge Tipi:

Makale

Yayın Tarihi:

9/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001300058

pISSN:

2148-4171