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Journals / Journal of Physical Chemistry and Functional Materials / 2024 / Volume: 7 Issue: 2

A New Hybrid Method for Classification of Rice Leaf Diseases: SVM+NCA+Resnet50

Harun Bingöl(MALATYA TURGUT ÖZAL ÜNİVERSİTESİ)0000-0001-5071-4616
Serpil Aslan(MALATYA TURGUT ÖZAL ÜNİVERSİTESİ, MÜHENDİSLİK VE DOĞA BİLİMLERİ FAKÜLTESİ)0000-0001-8009-063X
Pages
—
Publication Date
2024-12-18
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AI Summary & Key Takeaways

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

Within the scope of this study, it is recommended that the diseases on rice leaves be classified using artificial intelligence techniques, without mixing them with each other, with very high accuracy values, and without any problems caused by humans.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Rice is extremely important for individuals and countries, both in terms of nutritional value and financial value.
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Key Findings & Data:It is necessary to protect such an important plant from diseases and increase the yield.
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Conclusion & Impact:Within the scope of this study, it is recommended that the diseases on rice leaves be classified using artificial intelligence techniques, without mixing them with each other, with very high accuracy values, and without any problems caused by humans.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Artificial Intelligence · Classification · Resnet50

Abstract

Rice is extremely important for individuals and countries, both in terms of nutritional value and financial value. It is necessary to protect such an important plant from diseases and increase the yield. However, early detection of diseases on plant leaves can prevent the spread of this disease and is also very important in terms of treating the plant. Artificial intelligence has become very popular in recent years thanks to its success in terms of disease classification. CNN architectures used in image classification perform very successful work. Within the scope of this study, it is recommended that the diseases on rice leaves be classified using artificial intelligence techniques, without mixing them with each other, with very high accuracy values, and without any problems caused by humans. With this proposed model, a support vector machine-based model is proposed that classifies five (5) of the most common rice diseases with a very high accuracy of %98.

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Cite This Article

Bingöl, H., Aslan, S. (2024). A New Hybrid Method for Classification of Rice Leaf Diseases: SVM+NCA+Resnet50. Journal of Physical Chemistry and Functional Materials, Volume: 7 Issue: 2. https://doi.org/10.54565/jphcfum.1499620

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