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Journals / Uludağ Üniversitesi Mühendislik Fakültesi Dergisi / 2023 / Cilt: 28 Sayı: 3

USING CONVOLUTIONAL NEURAL NETWORK FOR GRAPE PLANT DISEASE CLASSIFICATION

Evrişimli Sinir Ağının Üzüm Bitkisi Hastalık Sınıflandırması için Kullanılması

Cemal İhsan Sofuoğlu(DOKUZ EYLUL UNIVERSITY)0009-0009-5280-5445
Derya Bırant(DOKUZ EYLUL UNIVERSITY)0000-0003-3138-0432
Pages
809–820
Publication Date
2023-12-27
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
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Key Findings & Data:This study proposes a new convolutional neural network model that accurately classifies the diseases on the plant leaves for the agriculture sectors.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Derin Öğrenme · Evrişimli Sinir Ağı · Görüntü Sınıflandırma

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.

Özet

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.

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Cite This Article

Sofuoğlu, C. İ., Bırant, D. (2023). USING CONVOLUTIONAL NEURAL NETWORK FOR GRAPE PLANT DISEASE CLASSIFICATION. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, Cilt: 28 Sayı: 3, 809–820. https://doi.org/10.17482/uumfd.1277418

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