Dikkat Modülleri ile Oluşturulmuş Derin Öğrenme Modelini Kullanarak Pamuk Hastalığının Tespiti

Year 2021, Volume: 36 Issue: 3, 659 - 668, 30.09.2021

Mesut Toğaçar

https://doi.org/10.21605/cukurovaumfd.1005343

Abstract

Pamuk, dünya genelinde önemli bir endüstri sektörü olup, tarıma dayalı ülkelerde ekonomik kalkınmanın en önemli faktörlerinden biridir. Ülkemiz, pamuk tarımına elverişli ülkeler arasında yer almaktadır ve genelde Akdeniz ile Güneydoğu Anadolu bölgesinde pamuk üretimi gerçekleştirilmektedir. Pamuk bitkisinden iç ve dış etmenlerden kaynaklı birçok hastalık görülebilmektedir. Araştırmacılar, pamuk hastalığının tespitini gerçekleştirmek ve verimli bir üretim elde edebilmek için son zamanlarda yapay zekâ tabanlı çalışmalara odaklanmışlardır. Bu çalışmada kullanılan veri kümesi; hastalıklı pamuk yaprağı, hastalıklı pamuk bitkisi, sağlam pamuk yaprağı ve sağlam pamuk bitki görüntülerinden oluşmaktadır. Önerilen yaklaşımda, veri büyütme tekniği ile dikkat modüllerinden oluşan derin öğrenme modeli birlikte kullanılmıştır. Çalışmanın analizlerinde, Olasılıksal Dereceli Azalma (ODA) ve Uyarlanabilir Moment Tahmini (UMT) optimizasyon yöntemleri kullanılmıştır. Sınıflandırma sürecinde elde edilen en iyi genel doğruluk başarısı %96,56 olmuştur.

Keywords

Derin öğrenme, Dikkat modülleri, Pamuk hastalığı, Veri büyütme

Detection of Cotton Disease Using Deep Learning Model Created with Attention Modules

Abstract

Cotton is an important industrial sector worldwide and is one of the most important factors of economic development in countries based on agriculture. Our country is among the countries that are suitable for cotton agriculture and cotton production is generally carried out in the Mediterranean and Southeast Anatolia. Many diseases caused by internal and external factors can be seen in cotton plants. Researchers have recently focused on artificial intelligence-based studies to detect cotton disease and achieve efficient production. The dataset used in this study; it consists of diseased cotton leaf, diseased cotton plant, fresh cotton leaf and disease fresh plant images. In the proposed approach, the data augmentation technique and the deep learning model consisting of attention modules are used together. Stochastic Gradient Descent (SGD) and Adaptive Moment Estimation (ADAM) optimization methods were used in the analysis of the study. The best overall accuracy success achieved in the classification process was 96.56%.

Keywords

Deep learning, Attention modules, Cotton disease, Data augmentation

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