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Journals / International Journal of Engineering and Innovative Research / 2019 / Volume: 1 Issue: 2

MUSHROOM SPECIES DETECTION USING IMAGE PROCESSING TECHNIQUES

Yasemin Rukiye Erkan(SELÇUK ÜNİVERSİTESİ, FEN BİLİMLERİ ENSTİTÜSÜ, BİLİŞİM TEKNOLOJİLERİ MÜHENDİSLİĞİ (YL) (TEZLİ))0000-0002-6843-3289
Humar Kahramanlı Örnek(SELÇUK ÜNİVERSİTESİ, TEKNOLOJİ FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ)0000-0003-2336-7924
Pages
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Publication Date
2019-12-16
DOI
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AI Summary & Key Takeaways

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

As a result of the research, K-NN algorithm achieved 80% and Naive Bayes algorithm achieved 96% accuracy.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:There are many kinds of mushrooms in the world, some of them are edible and some are poisonous.
📊
Key Findings & Data:People may want to eat the mushrooms they encounter in nature, as a result of which they may become poisoned or even die.
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Conclusion & Impact:As a result of the research, K-NN algorithm achieved 80% and Naive Bayes algorithm achieved 96% accuracy.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Image Processing · Mushroom Species · K-NN

Abstract

There are many kinds of mushrooms in the world, some of them are edible and some are poisonous. People may want to eat the mushrooms they encounter in nature, as a result of which they may become poisoned or even die. In this research image processing techniques, K-NN and Naive Bayes algorithms were used to classify mushroom species in Selçuk University Campus. As a result of the research, K-NN algorithm achieved 80% and Naive Bayes algorithm achieved 96% accuracy.

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Cite This Article

Erkan, Y. R., Örnek, H. K. (2019). MUSHROOM SPECIES DETECTION USING IMAGE PROCESSING TECHNIQUES. International Journal of Engineering and Innovative Research, Volume: 1 Issue: 2. https://ajindex.com/articles/1495573

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