Derin Transfer Öğrenmeye Dayalı Pirinç Bitkisi Hastalıklarının Tespiti

Abstract

Çeltiğin işlenmesi sonucu elde edilen pirinç, dünyada en çok tüketilen gıda ürünlerinden bir tanesidir. Bitki yapraklarında özellikle, çeltik yapraklarında oluşan hastalıklar çiftçilerin karşılaştığı önemli sorunlardan biridir. Çeltik bitkisi hastalıkları uzman bilgisi gerektirdiğinden dolayı zor bir problemdir. Çiftçiler mahsul hastalıkları hakkında yeterince bilgi sahibi olmadıklarından dolayı hastalık için yanlış tespit yapılmakta ve yanlış tedavi uygulanmaktadır. Hastalıkların hızlı ve doğru olarak tanınması, sağlıklı ve verimli üretimin artmasındaki en önemli süreçtir. Bu tür problemlere çözüm sunmak amacıyla çeltik bitkisi hastalıklarını tespit eden derin öğrenme tabanlı bir model geliştirilmiştir. Dört farklı çeltik bitkisi hastalığı içeren veri kümesi üzerinde model eğitilmiş ve 0,0014 kayıp değeri ile başarılı bir model oluşturulmuştur. Eğitilmiş ImageNet modelleri üzerinde transfer öğrenme metodu ile modeller oluşturmak için dört farklı derin öğrenme algoritması kullanılmış ve bu modellerin performansları karşılaştırılmıştır. En başarılı model VGG16 transfer öğrenme mimarisi ile elde edilmiştir. Deneysel sonuçlar, bu çalışmada önerilen transfer öğrenme yönteminin pirinç yaprağı hastalıklarını tanıyabildiğini ve bunun da birçok bitkinin yaprak hastalıklarını tanımak için güvenilir bir yöntem sağladığını göstermektedir.

Keywords

• çeltik bitkisi hastalıkları
• derin öğrenme
• transfer öğrenme

Detection of Rice Plant Diseases Based on Deep Transfer Learning

Abstract

Rice, obtained through the processing of paddy, is one of the most widely consumed food products globally. However, diseases affecting rice plants, particularly those occurring on the rice leaves, pose significant challenges for farmers. The identification of rice plant diseases demands specialized knowledge, making it a complex issue to tackle. Often, due to insufficient understanding, farmers misdiagnose diseases and apply incorrect treatments. Rapid and accurate disease diagnosis plays a pivotal role in enhancing healthy and productive crop cultivation. To address this problem, a deep learning-based model was developed to detect rice plant diseases. The model was trained on a dataset containing four different rice plant diseases and achieved a successful outcome with a loss value of 0.0014. Additionally, four different deep learning algorithms were used to create models through transfer learning with pre-trained ImageNet models, and a comparison of their performance was presented. The most successful model was obtained using the VGG16 transfer learning architecture. Experimental results in this study demonstrate that the proposed transfer learning method can effectively recognize rice leaf diseases, providing a reliable approach for identifying leaf diseases in various plants.

Keywords

• rice plant diseases
• deep learning
• transfer learning

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