USING CONVOLUTIONAL NEURAL NETWORK FOR GRAPE PLANT DISEASE CLASSIFICATION

Year 2023, Volume: 28 Issue: 3, 809 - 820, 27.12.2023

Cemal İhsan Sofuoğlu Derya Bırant

https://doi.org/10.17482/uumfd.1277418

Abstract

Plant disease classification is the use of machine learning techniques for determining the type of disease from the input leaf images of the plants based on certain features. It is an important research area since early identification and treatment of plant disease is critical for saving crops, preventing agricultural disasters, and improving productivity in agriculture. This study proposes a new convolutional neural network model that accurately classifies the diseases on the plant leaves for the agriculture sectors. It especially works on the classification of plant diseases for grape leaves from images by designing a deeplearning architecture. A web application was also implemented to help the agricultural workers. The experiments carried out on real-world images showed that a significant improvement (8.7%) on average was achieved by the proposed model (98.53%) against the state-of-the-art models (89.84%) in terms of accuracy.

Keywords

Deep Learning, Convolutional Neural Network, Image Classification, Agriculture, Grape, Plant Disease

Evrişimli Sinir Ağının Üzüm Bitkisi Hastalık Sınıflandırması için Kullanılması

Abstract

Bitki hastalık sınıflandırması, belirli özelliklere dayalı olarak bitkilerin yaprak görüntülerinden hastalık türünün belirlenmesi için makine öğrenmesi tekniklerinin kullanılmasıdır. Bitki hastalıklarının erken teşhisi ve tedavisi, ekinleri kurtarmak, tarımsal felaketleri önlemek ve tarımda verimliliği artırmak için kritik olduğundan, önemli bir araştırma alanıdır. Bu çalışma, tarım sektörü için bitki yapraklarındaki hastalıkları doğru bir şekilde sınıflandıran yeni bir evrişimli sinir ağı modeli önermektedir. Bir derin öğrenme mimarisi tasarlayarak özellikle üzüm yapraklarındaki hastalıkların sınıflandırılması üzerine çalışmaktadır. Tarım işçilerine yardımcı olması için bir web uygulaması da geliştirilmiştir. Gerçek dünya görüntüleri üzerinde yapılan denemeler, önerilen modelin (%98,53) doğruluk açısından son teknoloji modellere (%89,84) göre ortalamada önemli bir iyileştirme (%8,7) sağladığını göstermiştir.

Keywords

Derin Öğrenme, Evrişimli Sinir Ağı, Görüntü Sınıflandırma, Tarım, Üzüm, Bitki Hastalığı

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